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Wang and Yang Int J Ment Health Syst (2018) 12:33 https://doi.org/10.1186/s13033-018-0212-4

International Journal of Mental Health Systems

Does chronic disease influence susceptibility to the effects of air pollution on depressive symptoms in China? Qing Wang1† and Zhiming Yang2*†

Background The Chinese government encouraged the growth of

industries and urbanization since 1978. However, the

associated rapid economic development has caused

environmental issues, and China is now one of the most

polluted countries in the world [1–3]. For example, the

annual average Total Suspended Particulates (TSP) con-

centration regularly exceeds 400 μg/m 3

in China [4],

which is significantly higher than that in large European

cities (e.g. Oslo, 15 μg/m 3 ; Marseille, 18 μg/m

3 ) [5] and

World Health Organization (WHO) primary (80 μg/m 3 )

and secondary (60 μg/m 3 ) standards [6, 7].

Air pollution is believed to be associated with depres-

sive symptoms [8]. Potential biological mechanisms

*Correspondence: yangzm@ustb.edu.cn †Qing Wang and Zhiming Yang are Joint first authors 2 Donlinks School of Economics and Management, University of Science

and Technology Beijing, Beijing 100083, China

Full list of author information is available at the end of the article

relating to depressive symptoms include reactivity to

exogenous stressors; alterations of neurohumoral,

immune, and autonomic regulation; dysfunction of

neuro transmitter systems; and oxidative stress [9]. Cell

© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 2 of 12

cultures and experimental animals studies have shown

neuropathological effects from air pollution exposure

[10–12], and previous empirical studies have observed

that air pollution increases the prevalence of depressive

symptoms in Korea, Japan, and the Netherlands [8, 13–

15]. Furthermore, an increasing number of emergency

department visits for depression in Canada and Korea

have been documented [16, 17].

Depression is a serious problem in China [18, 19 ]: in

2013, 36 million years of healthy life were lost to mental

illness in China, and estimates suggest that by 2025, 39.6

million years of healthy life will be lost (10% increase)

[20]. Although little is known about the asso- ciation

between air pollution and depression, several Chinese

studies have found a relationship between expo- sure to air

pollution and happiness [21, 22], depressive symptoms

[23], cognitive functions [24, 25] and hospi- tal admissions

for mental disorders [26], when results were adjusted for

demographics and socioeconomic status. It is considered

that a further decline in air qual- ity could cause an

increased risk to health and an asso- ciated increase in

depressive symptoms. Therefore, this study uses nationally

representative data to estimate the association between air

pollution and depression meas- ured by the Center for

Epidemiologic Studies Depression (CES-D) scale.

Air pollution regulations based on observed health

effects in the general population may be insufficient to

protect exceptionally vulnerable subgroups. Incon-

sistent study results have been found within past study

cohorts. For example, no significant association

between air pollution exposure and depressive symp-

toms was found in a Boston-area study [27], although

other American studies reported that exposure to air

pollution was related to anxiety symptoms [28], which

often have a comorbidity with depression [29]. One

American study found that stroke victims were more

susceptible to the effects of air pollution with respect to

cognitive functions [30]. Thus, it is believed that

chronic disease (e.g. hypertension), which is often

regarded as a marker of inflammation and vascular

dysfunction, may mediate an association between air

pollution and depressive symptoms [31, 32]. Compared

to people in good physical health, the well-known

adverse mental health effects of air pollution may mean

that respondents with chronic disease are likely to

believe that their physical health is being damaged [33–

35]. However, the role of an individual’s physical health

status in the association between air pollution and

depression symptoms has not yet been addressed in

China. Therefore, this study aims to assess which

individuals have a greater vulnerability to the adverse

effects of air pollution [21].

Data and methods Data

Individual sample data and a group of city-level vari- ables

were obtained to evaluate the relationship between air

pollution, chronic disease, and depressive symptoms.

Individual data were collected from CHARLS 2011 and

2013, which were national representative surveys con-

ducted with middle-aged and elderly Chinese residents

(aged 45 years and above) using face-to-face computer-

assisted personal interviews. The CHARLS question- naire

included the following modules: demographics, family

structure/transfer, health status and functioning,

biomarkers, health care and insurance, work, retirement

and pension, income and consumption, assets (individual

and household), and community-level information. These

surveys were approved by the ethics committee of the

Institutional Review Board of Peking University.

Using multi-stage stratified probability-proportionate-

to-size sampling, the sample in CHARLS represented

approximately 10,000 households in 150 counties/dis-

tricts (a total of 450 villages/resident communities). The

baseline survey was conducted between June 2011 and

March 2012 with a response rate of 80.5% and a total

sample of 17,545 respondents. A total of 15,020 (86%)

respondents participated in the follow-up survey in 2013,

but 2525 (14%) respondents had died or declined partici-

pation in the study. In this study, CHARLS 2011 and 2013

panel data were constructed to estimate the relation- ship

between air pollution and depressive symptoms for

15,020 respondents (15,020 × 2 = 30,040 samples). Of the respondents, 47% were male with a mean age of 60 years.

Ages and gender distribution were very similar to those

in the 6th national census conducted in 2010 [36].

City-level variables included monthly temperature,

monthly relative humidity, and annual air pollution. Daily

meteorological data from 839 stations in 2011 and 2013

were collected from the China Meteorological Science

Data Sharing Service Network—China Ground Climate

Daily Data. The station-level data were aggregated at a city

level by matching stations to the closest city based on the

exact longitude and latitude of the weather station and the

longitude and latitude of the county centroid. The average

monthly temperature and relative humidity of CHARLS

125 sample cities were then calculated from daily data.

Based on survey city and month, the results from 15,020

respondents in the two CHARLS waves were combined

with the meteorological data from 125 cities.

The annual sulfur dioxide (SO2) and TSP emissions from

273 cities in 2011 and 2013 were obtained from the 2012

and 2014 China City Statistical Yearbook. Based on survey

year and city, 12,046 respondents of the 15,020

respondents in the two CHARLS waves were matched to

the air pollution data from 101 cities. After excluding 412

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 3 of 12

respondents that provided missing values from 12,046

respondents, 11,634 respondents were included, and the

final sample size was 23,268 (11,634 × 2) samples from

101 cities. Figure 1 presents a flow chart of the study

process.

Variables

Depressive symptoms

A modified seven-item version of the CES-D scale was

constructed to measure depressive symptoms [37].

Respondents reported the frequency of experiencing the

following seven depressive symptoms during the past

week: (1) “was bothered by things,” (2) “had trouble keep-

ing mind on what was doing,” (3) “felt depressed,” (4) “felt

everything he/she did was an effort,” (5) “felt fearful,” (6)

“sleep was restless,” and (7) “felt lonely.” Each answer was

encoded from 1 to 4: 1 = rarely or none, 2 = some or a lit-

tle, 3 = occasionally or a moderate amount, and 4 = most or all of the time, with total scores ranging from 7 to

28. A summed score of the seven items was calculated,

with lower scores indicating fewer depressive symptoms.

This shortened seven-item CES-D scale is a widely used

indicator for depressive symptoms [38, 39]. Its validity,

reliability, and cultural equivalence have been proven in

China [40]. In our data set, CES-D was also demon- strated

to have high internal consistency (Cronbach’s

alpha = 0.82) and to construct validity (Kaiser–Meyer–

Olkin = 0.87) according to the standards of Meulen et al. [41], Kara [42], and Aly [43], which suggest that if the

Cronbach’s alpha and Kaiser–Meyer–Olkin test value

exceeds the recommended level of 0.70 then data is con-

sidered to be highly reliable [41–43].

Air pollution

Following previous studies, SO2 and TSP emission inten-

sity, SO2 and TSP emissions per capita, and SO2 and TSP

emissions per unit area were calculated to measure air pol-

lution [44–47]. Pollution intensity refers to the indicator of

pollution emissions per industrial gross domestic product

(GDP) (industrial economic output). Log transformation of

air pollution data was applied to minimize skewness [45].

Chronic disease

A categorical variable for a doctor to use in diagnosing

chronic disease was created based on the question, “Have

you ever been diagnosed with hypertension, dyslipidemia,

diabetes or high blood sugar, cardiovascular disease (heart

attack, coronary heart disease, angina, congestive heart

failure, stroke or other heart problems), cancer or malignant

tumor, liver disease, chronic lung disease, kid- ney disease,

stomach or other digestive disease, arthritis or rheumatism

and asthma by a doctor?” The variable equaled 1 or 0 for

respective replied of “yes” or “no”.

Estimation Strategy

Descriptive analysis was first conducted to describe sam-

ple characteristics of the total sample and for chronic dis-

ease status. Frequencies with percentages were presented

for categorical variables (gender, marital status, educa-

tion, employment status, insurance status) and means

with standard deviations for continuous variables (CES-

D, air pollution indicators, climate indices, income, and

age). P-values were calculated using the Chi square test

for categorical variables, and one-way Analysis of Vari-

ance (ANOVA) for continuous variables between groups

(with or without chronic disease).

A random effects model was then applied to link air pol-

lution intensity with depressive symptoms. Omitted varia-

ble bias was controlled using the random effects model. The

individual random effects model is presented as follows,

Depressiveijt = a0 + Airjta1 + Xijta2 + ui + vt + eijt ,

(1)

where a0, a1, a2 are the parameters to be estimated; ui and vt

are the individual effects and year fixed effects, respec-

tively; eijt is the idiosyncratic error term; and Depres- siveijt

is the depressive symptoms of person i in city j in year t;

Airjt is a variable indicating the log of air pollution

intensity, Xijt represents an individual’s demographic,

socioeconomic status, health behavior, and city-level cli-

mate variables in the living area. To be more specific, the

demographic variables included whether male or female,

marital status [reference group: married with spouse

present (common-law marriage was considered mar- ried)],

and age; socioeconomic status was measured by

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 4 of 12

ijt

ijt

ijt

ijt

ijt

ijt

household income per capita, education level, employ-

ment status, insurance status, and rural/urban residence;

educational attainment was defined at four levels (infor-

mal education, informal education but can read and

write, primary school, and junior high school and above),

and a categorical variable for educational attainment

with four values was constructed, with informal educa-

The total CES-D scale used to evaluate depressive

symptoms of middle-aged and elderly individuals ranged

between 7 and 28, which represents a limited dependent

variable. Therefore, least square regression was directly

applied to render inconsistent estimates [50, 51]. The Tobit

model was used for robust analysis in this respect, and

marginal effects were reported,

tion serving as the reference group; household income

was divided by the number of household members and

household income per capita was subsequently ranked

and divided into five pentiles, with the lowest group

Depressive ∗

= c0 + Airjtc1 + Chronicijtc2 + Airjt

× Chronicijtc3 + Xijtc4 + ui + vt + eijt , (3)

serving as a reference; employment status was catego-

rized into three groups: unemployed (including retired), Depressiveijt = 7 if Depressive

∗ ≤ 7, (4)

self-employed, and wage earner; for health insurance

coverage, respondents were recoded into a dummy vari-

able with three values [the urban employee-based basic

Depressiveijt = Depressive ∗

if 7 < Depressive ∗

< 28,

(5)

medical insurance scheme (UEBMI), the rural new coop-

erative medical scheme (NCMS), and the urban resident- Depressiveijt = 28 if Depressive

∗ ≥ 28, (6)

based basic medical insurance scheme (URBMI)], with where c0, c1, c2, c3, c4 are the parameters to be estimated;

uninsured respondents as the reference group; current Depressive∗ is a latent variable and Depressiveijt is its

smoker and drinker were included as indicators of the

respondents’ current health behaviors; and city-level

average monthly temperature, relative humidity, and city

dummy variables were also controlled.

The interaction between chronic disease and air pollu-

tion intensity was then controlled in multivariate regres-

sion to establish whether chronic disease influences an

individual’s susceptibility to depressive symptoms with

respect to air pollution,

Depressiveijt = b0 + Airjtb1 + Chronicijtb2 + Airjt

× Chronicijtb3 + Xijtb4 + ui + vt + eijt , (2)

where b0, b1, b2, b3, b4 are the parameters to be esti-

mated; Chronicijt is a dummy variable indicating whether

a respondent has a chronic disease; and Airjt× Chronicijt

is the interaction between chronic disease and air pol-

lution intensity after decentralization. Decentralization

of air pollution indicators was calculated by subtracting

the mean of city-level air pollution intensity from air pol-

lution intensity in each city and then dividing it by the

standard deviation of city-level air pollution intensity

using the center-command in Stata 14 [48].

Analyses were then stratified according to chronic

disease characteristics. Depressive symptoms may also

affect an individual’s physical health status and lead to

endogenous issues [49]. Stratification was conducted to

eliminate any possible endogenous issues by excluding

respondents with chronic diseases (hypertension, dys-

lipidemia, diabetes or high blood sugar, cardiovascular

diseases, arthritis and asthma) because such diseases may

stem from depressive symptoms. Under these conditions

no other possible methods were available for now.

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 5 of 12 observable variable. A robust standard error was

derived using bootstrapping methods and conducting

500 boot- strap replications.

In addition, by using the Tobit model with stratified

samples, the effects of SO2 and TSP emission per capita/

per unit area on depression were regressed (and are pre-

sented in Appendix: Table 4). The results were

consistent with those using air pollution emission

intensity. Stata version 14 was used for all analyses [48].

Results Participant characteristics and average air pollutant

intensity across chronic disease are shown in Table 1.

On average, SO2 and TSP emissions accounted for

82.950 (SD = 78.355) and 45.571 (SD = 49.025) tons per

100 million Chinese yuan of industrial GDP, respec-

tively. SO2 emissions per unit area and per capita were

6.812 (SD = 8.307) tons/km2 and 135.137 (SD = 120.211)

tons/10,000 people; and TSP emissions per unit area and per capita were 3.079 (SD = 3.495) tons/km2 and 70.938

(SD = 81.064) tons/10,000 people; and average tem-

perature and humidity were 26.599 °C (SD = 3.935) and

73.253% (SD = 7.603), respectively.

The mean age of respondents was 60 (SD = 9.989) years; 48% of respondents (11,108/23,268) were male;

88% (20,414/23,268) were married or cohabit- ing; 39%

(9016/23,268) lived in an urban area; 44%

(10,191/23,268) of respondents had no formal edu-

cation; 33% (7734/23,268) were unemployed; 44%

(10,162/23,268) were self-employed; and the major-

ity had health insurance [94% (21,813/23,268)]. A total

of 33% (7713/23,268) reported smoking and 33%

(7756/23,268) reported drinking alcohol. The

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 6 of 12

a Frequencies with percentages were presented for categorical variables. P-values were calculated by Chi square test between groups having chronic disease or not

b Means with standard deviations were presented for continuous variables, and P-values were calculated one-way ANOVA between groups having chronic disease or not

respondents earned an average of 8175 (SD = 14,912)

Chinese yuan per year per capita. The average depres-

sive symptoms score was 11.623 (SD = 4.664). Com-

pared to participants with chronic disease, those without

chronic disease were more likely to report lower

depressive symptoms [12.176 (SD = 4.858) versus

10.538 (SD = 4.043)] but not more likely to be exposed

to air pollution.

Models 1 and 3 from Table 2 show the correlation

between air pollution and depressive symptoms in China

after adjusting for multiple covariates. Increasing lev- els

of air pollution were found to be significantly asso- ciated

with higher depressive symptoms: an increase in SO2 and

TSP emission intensities of 1% was associated with

increasing depressive symptoms scores by 1.266

(SE = 0.107, P < 0.001, 95% CI 1.057–1.475) and 1.318

Table 1 Statistical description

Variable All sample (N = 23,268)

Group with chronic disease (N = 15,412)

Group without chronic disease (N = 7856)

Group with chronic disease VS Group without chronic disease

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. P-valuea

Air pollution emission intensity (tons per 100 million Chinese yuan)

SO2 82.950 78.355 82.602 74.980 83.632 84.587 < 0.001

TSP 45.571 49.025 46.510 50.816 43.729 45.253 < 0.001

Air pollution emission per unit area (tons/km2)

SO2 6.812 8.307 6.667 8.728 7.097 7.403 < 0.001

TSP 3.079 3.495 2.998 3.576 3.236 3.326 < 0.001

Air pollution emission per capita (tons/10,000 people)

SO2 135.137 120.211 132.127 118.256 141.042 123.753 < 0.001

TSP 70.938 81.064 70.173 80.964 72.440 81.243 < 0.001

Climatic indexes

Average monthly temperature (0.1 °C) 265.990 39.353 266.038 39.678 265.894 38.710 < 0.001

Average monthly relative humidity (%) 73.253 7.603 73.077 7.634 73.599 7.529 < 0.001

Depressive symptoms 11.623 4.664 12.176 4.858 10.538 4.043 < 0.001

Age 60.101 9.989 61.101 9.834 58.139 10.000 < 0.001

Income (Chinese yuan/year) 8175 14,912 7970 13,343 8576 17,583 0.011

n % n % n % P-valueb

Male 11,108 47.739 7165 46.490 3943 50.191 0.010

Unmarried 2854 12.266 1991 12.919 863 10.985 0.022

Living in urban area 9016 38.748 5947 38.587 3069 39.066 0.730

Education < 0.001

No education 5973 25.670 4145 26.895 1828 23.269

No education but can read/write 4218 18.128 2946 19.115 1272 16.191

Primary school 5181 22.267 3498 22.696 1683 21.423

Junior high school and above 7896 33.935 4823 31.294 3073 39.117

Employment status < 0.001

Unemployed 7734 33.239 5623 36.485 2111 26.871

Self-employed 10,162 43.674 6710 43.537 3452 43.941

Wage earner 5372 23.087 3079 19.978 2293 29.188

Insurance 0.037

Uninsured 1455 6.253 854 5.541 601 7.650

NCMS and URBMI 18,635 80.089 12,389 80.385 6246 79.506

UEBMI 3178 13.658 2169 14.074 1009 12.844

Health behavior

Current Smoker 7713 33.149 4757 30.866 2956 37.627 < 0.001

Current Drinker 7756 33.333 4932 32.001 2824 35.947 0.005

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 7 of 12

Table 2 Association of air pollution intensity and depressive symptoms and the role of chronic disease (N = 23,268)

Variables Influence of SO2 emission intensity on depressive symptoms

Influence of TSP emission intensity on depressive symptoms

Model 1 Model 2 Model 3 Model 4

Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.

Log of SO2 intensity 1.266*** 0.107 1.093*** 0.116 – – – –

Log of TSP intensity – – – – 1.318*** 0.082 1.115*** 0.092

Chronic disease – – 1.384*** 0.068 – – 1.388*** 0.068

Log of SO2 intensity × Chronic disease – – 0.217*** 0.084 – – – –

Log of TSP intensity × Chronic disease – – – – – – 0.281*** 0.071

Log of average monthly temperature 0.189** 0.085 0.176** 0.085 0.201** 0.087 0.191** 0.087

Log of average monthly relative humidity − 0.690** 0.333 − 0.693** 0.332 − 0.445 0.335 − 0.451 0.333

Age group

50–59 0.248*** 0.090 0.134 0.089 0.263*** 0.090 0.148* 0.089

60–69 0.148 0.105 − 0.057 0.104 0.174* 0.104 − 0.031 0.103

More than 70

Male

− 0.396***

1.270***

0.131

0.091

− 0.606***

1.269***

0.130

0.090

− 0.368***

1.282***

0.131

0.091

− 0.578***

1.283***

0.130

0.090

Unmarried 0.680*** 0.121 0.701*** 0.119 0.682*** 0.121 0.701*** 0.119

Living in urban area

Education

− 0.400*** 0.104 − 0.398*** 0.103 − 0.395*** 0.104 − 0.387*** 0.103

No education but can read/write 0.414*** 0.118 0.373*** 0.116 0.416*** 0.118 0.376*** 0.116

Primary school − 0.043 0.112 − 0.081 0.110 − 0.039 0.112 − 0.077 0.110

Junior high school and above − 0.470*** 0.114 − 0.454*** 0.112 − 0.461*** 0.114 − 0.448*** 0.112

Employment status

Self− employed − 0.165* 0.089 − 0.109 0.088 − 0.152* 0.088 − 0.096 0.088

Wage earner − 0.351*** 0.090 − 0.262*** 0.089 − 0.354*** 0.090 − 0.265*** 0.089

Insurance

NCMS and URBMI − 0.061 0.130 − 0.123 0.129 − 0.052 0.129 − 0.113 0.128

UEMBI − 0.432*** 0.152 − 0.520*** 0.151 − 0.418*** 0.151 − 0.503*** 0.150

Income group

21–40th percentile 0.328*** 0.100 0.335*** 0.099 0.340*** 0.100 0.349*** 0.099

41–60th percentile 0.078 0.096 0.068 0.095 0.091 0.096 0.082 0.095

61–80th percentile − 0.073 0.096 − 0.068 0.095 − 0.063 0.096 − 0.056 0.095

81–100th percentile − 0.289*** 0.101 − 0.296*** 0.100 − 0.288*** 0.101 − 0.295*** 0.100

Health behavior

Current drinker 0.040 0.072 0.108 0.072 0.0459 0.072 0.114 0.072

Current smoker

Constant

− 0.196**

7.165***

0.081

1.548

− 0.156*

7.025***

0.080

1.562

− 0.186**

7.219***

0.081

1.538

− 0.144*

7.079***

0.080

1.544

City dummy variables YES YES YES YES

Adjusted R2 0.139 0.165 0.139 0.165

Wald Chi square 2126*** 2596*** 2228*** 2707***

Models 1–4 are estimated using the xi:xtreg-command in Stata 14. Decentralization was calculated using the center-command

* P < 0.10; ** P < 0.05; *** P < 0.01

(SE = 0.082, P < 0.001, 95% CI 1.157–1.480), respec- tively. Models 2 and 4 from Table 2 present the interac-

tion between air pollution and chronic disease and their

effect on depressive symptoms. After controlling for the

interaction of air pollution and chronic disease, a posi-

tive correlation between air pollution and depressive

symptoms was observed, as expected. An 1% increase

in the intensities of SO2 and TSP emissions was associ-

ated with 1.237 (1.093 (SE = 0.116, P < 0.001, 95% CI

0.866–1.320) + 0.217 (SE = 0.084, P = 0.009, 95% CI

0.053–0.380) × 15,412/23,268 = 1.093 + 0.217 × 66%)

and 1.301 (1.115 (SE = 0.092, P < 0.001, 95% CI 0.934–

1.296) + 0.281 (SE = 0.071, P < 0.001, 95% CI 0.143–

0.420) × 66%) higher depressive symptoms scores,

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 8 of 12

respectively. In addition, due to an 1% increase in the

intensities of SO2 and TSP emissions, the depressive

symptoms scores for respondents with chronic disease

increased by 1.903 (1.384 (SE = 0.068, P < 0.001, 95%

CI 1.250–1.518) + 0.217 (SE = 0.084, P = 0.009, 95% CI 0.053–0.380) × log(82.950)) and 1.854 (1.388 (SE = 0.068,

P < 0.001, 95% CI 1.254–1.522) + 0.281 (SE = 0.071,

P < 0.001, 95% CI 0.143–0.420) × log(45.571)), respec- tively. Given the same intensities of SO2 and TSP emis-

sions, respondents with chronic disease had higher scores

for depressive symptoms by 0.217 (SE = 0.084, P = 0.009,

95% CI 0.053–0.380) and 0.281 (SE = 0.071, P < 0.001,

95% CI 0.143–0.420) than those without chronic disease.

Models 1–4 from Table 3 show the results strati- fied

using chronic disease characteristics; models 5–8 show

results using the Tobit model; models 9–12 show the

results stratified using chronic disease character- istics

and the Tobit model. Robust analysis shows that the

results obtained were consistent with those using the

random effects model. According to models 10 and 12

from Table 3, for individuals with cancer or malig- nant

tumor, chronic lung diseases, liver diseases, kid- ney

disease, and stomach diseases, when the SO2 and TSP

emission intensities increased by 1% individu-

als showed an increase in depressive symptom scores

of 0.844 (0.788 (SE = 0.128, P < 0.001, 95% CI 0.537–

1.039) + 0.221 (SE = 0.091, P = 0.015, 95% CI 0.044–

0.399) × 2669/10,508 = (0.788 + 0.221 × 25%) and 0.818

(0.765 (SE = 0.118, P < 0.001, 95% CI 0.534–0.997) + 0.208

(SE = 0.107, P = 0.051, 95% CI 0–0.417) × 25%), respec- tively. In addition, due to an 1% increase in the intensi- ties

of SO2 and TSP emissions, respondents with these chronic

diseases scored higher for depressive symp- toms by

1.292 (0.869 (SE = 0.102, P < 0.001, 95% CI

0.668–1.069) + 0.221 (SE = 0.091, P = 0.015, 95% CI 0.044–0.399) × log (82.000)) and 1.208 (0.866 (SE = 0.114,

P < 0.001, 95% CI 0.643–1.090) + 0.208 (SE = 0.107,

P = 0.051, 95% CI 0–0.417) × log(43.885)), respectively. Given the same intensities of SO2 and TSP emissions,

individuals with chronic disease had higher scores for

depressive symptoms by 0.221 (SE = 0.091, P = 0.015, 95%

CI 0.044–0.399) and 0.208 (SE = 0.107, P = 0.051, 95% CI 0–0.417) than those without chronic disease. However,

the impacts of air pollution were reduced after eliminat-

ing the endogenous variable, which supports the hypoth-

esis that depressive symptoms influence physical health.

Discussion To the best of the authors’ knowledge, this is the first

published article to elucidate the role of chronic disease

in an association between air pollution and depressive

symptoms within the Chinese population, who prefer

to acknowledge poor mental conditions instead of men-

tal illness. Using nationally representative data for the

general Chinese middle- and old-aged population, this

study found that exposure to air pollution was related to

depression and that if an individual had a chronic disease,

they were more vulnerable to the depressive symptoms

effects of air pollution. These findings provide a com-

prehensive understanding of the extent that air pollution

affects depression, which could provide valuable insights

for the design of policies and promotional programs to

enhance the quality of mental health and curb the nega-

tive effects of air pollution in China.

The results of a positive association between air pollu-

tion and depression are consistent with those of previous

Chinese studies and results from Japan, Korea, Canada,

and the Netherlands [8, 13–17], but are contradictory to

results from Norway and Boston [27, 52]. This discrepancy

could possibly be related to the intensity of air pollution

[52, 53]. For example, air pollution in China occurs at some

of the highest levels in the world: in Yale University’s 2016

Environmental Performance Index, China is ranked 109

out of 180 countries [54]. Therefore, insignificant results

from countries with low pollution levels merely indicate

that a minor amount of air pollution is not harmful with

respect to depression, whereas levels are high in China, are

causing health issues, and are continuing to rise.

This study also identifies susceptible subgroups and

extends research on the subject of adverse mental health

effects relating to air pollution by testing the assumption

that individuals with chronic disease were more vulner-

able to depressive symptoms effects relating to air pol-

lution. It is of note that our study found that individuals with

cardiovascular disease were more likely to report

depressive symptoms related to air pollution compared to

those without chronic disease, even when results were

adjusted for other factors. While no studies have inves-

tigated the role of air pollution and depression relating to

chronic disease, the relationship determined here is

consistent with results of studies showing a relationship

between (1) exposure to ambient air pollution and an

increase in chronic mortality and morbidity [55–65]; (2)

chronic disease and depressive symptoms [66].

Considering the large variation in optimal thresholds for

identifying depressive symptoms in self-reported responses,

this study adopted a continuous variable to measure depres-

sive symptoms, instead of using a cut-off point to dichoto-

mously distinguish depressive symptoms [67]. In doing so,

caution is also warranted when attaching pathological labels

to self-reported symptoms [68]. A dimensional scale with

higher scores indicating more symptoms provides enhanced

clarification of depressive symptoms [69], particularly for

individuals with a Chinese cultural background where

depressive symptoms are highly stigmatized [55, 70].

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 9 of 12

Table 3 Association of air pollution intensity and depressive symptoms and the role of chronic disease: Robustness test

Variables Influence of SO2 emission intensity on depressive symptoms Influence of TSP emission intensity on depressive symptoms

Model 1

Model 2

Model 3 Model 4

Coef. Std. Err.

Coef. Std. Err.

Coef. Std. Err.

Coef. Std. Err.

Association of air pollution intensity and depressive symptoms and the role of chronic disease among respondents without ment al-related chronic disease

(N = 10,508)

Log of SO2 intensity 0.952*** 0.141 0.901*** 0.145 – – – –

Log of TSP intensity – – – – 0.929*** 0.115 0.875*** 0.118

Chronic disease – – 0.993*** 0.113 – – 0.991*** 0.113

Log of SO2 inten- – – 0.256* 0.138 – – – –

sity × chronic disease

Log of TSP inten- – – – – – – 0.240** 0.118

sity × chronic disease

Adjusted R2 0.123 0.139 0.123 0.140

Wald Chi square 862*** 961*** 876*** 974***

Model 5 Model 6 Model 7 Model 8

Marginal effects Boot. Std.

Marginal effects Boot. Std.

Marginal effects Boot. Std.

Marginal effects Boot. Std.

Association of air pollution intensity and depressive symptoms and the role of chronic disease: Tobit model (N = 23,268)

Log of SO2 intensity 1.122*** 0.101 0.969*** 0.090 – – – –

Log of TSP intensity – – – – 1.169*** 0.071 0.989*** 0.068

Chronic disease – – 1.227*** 0.056 – – 1.230*** 0.065

Log of SO2 inten- – – 0.191*** 0.063 – – – –

sity × chronic disease

Log of TSP inten- – – – – – – 0.248*** 0.061

sity × chronic disease

Sigma(u) 2.484*** 0.054 2.406*** 0.038 2.491*** 0.042 2.414*** 0.040

Sigma(e) 3.663*** 0.029 3.661*** 0.024 3.653*** 0.037 3.650*** 0.030

Log likelihood − 67,019 − 66,835 − 66,985 − 66,798

Model 9 Model 10 Model 11 Model 12

Marginal effects Boot. Std.

Marginal effects Boot. Std.

Marginal effects Boot. Std.

Marginal effects Boot. Std.

Association of air pollution intensity and depressive symptoms and the role of chronic disease among respondents without ment al-related chronic disease:

Tobit model (N = 10,508)

Log of SO2 intensity 0.832*** 0.122 0.788*** 0.128 – – – –

Log of TSP

Chronic disease – – 0.869*** 0.102 – – 0.866*** 0.114

Log of SO2 sity × chr

Log of TSP

sity × chr

Sigma(u)

Sigma(e)

Log likelihood − 29,353 − 29,307 − 29,344 − 29,297

Models 1–4 are estimated using the xi:xtreg-command in Stata 14, models 5–12 are estimated using the xi:xttobit-command. Decentralization was calculated using the center-command, and Marginal effects was calculated using the margins-command

Control variables included individual’s demographic, socioeconomic status, health behaviors and city-level climate variables in living areas. City dummy variables were also controlled

* P < 0.10; ** P < 0.05; *** P < 0.01

Nevertheless, the limitations of this study should be con-

sidered before discussing potential policy implications. The

previous studies proved that other contaminants such as

PM10 or PM2.5 may also be related to mental health effects

[52]. However, use of these air pollution compounds was

restricted in our analysis due to data constraints. A further

limitation relating to data constraints is that only annual

air pollution intensity data were available for use, which

intensity – – – – 0.812*** 0.102 0.765*** 0.118

inten- – onic disease

– 0.221** 0.091 – – – –

inten- – onic disease

– – – – – 0.208** 0.107

2.098*** 0.068 2.052*** 0.064 2.103*** 0.071 2.056*** 0.070

3.440*** 0.042 3.441*** 0.050 3.434*** 0.038 3.435*** 0.048

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 10 of 12

cannot be fully correlated with weekly depression symp-

toms. Although such data and associated results are useful

in that depressive symptoms may result from accumulated

exposure to air pollution [53], it is not possible to untan-

gle short-term effects from long-term effects in this study.

However, although caution should be taken when explain-

ing the results, it is clear that high levels of SO2 and TSP

within lead to depression.

An underlying endogeneity problem may exist if

depressive symptoms also affect an individual’s chronic

disease status, which would lead to a simultaneity issue

[49, 71, 72]. For example, a depressed patient could

become trapped in a negative cycle in which men- tal

symptoms are exacerbated by the synergistic effect of

stress and cardiovascular risk factors, or where a patient

is vulnerable to acute cardiovascular events due to the

synergistic effect of mental stress and an under- lying

atherothrombotic disorder [72]. It is considered that

depressive symptoms can have an influence on the

occurrence of hypertension, dyslipidemia, diabetes or

high blood sugar, cardiovascular diseases, arthritis and

asthma [71–83]. However, our individual level data relied

on retrospective self-evaluation, which is potentially

endogenous due to measurement errors. Although quasi-

experimental or instrument variables could have cor-

rected the bias [84] these corrections are now unavailable

because of data limitations. The analyses presented here

focused on a subsample of the population that excluded

respondents suffering from chronic diseases stemming

from depressive symptoms. To be specific, respondents

with hypertension, dyslipidemia, diabetes or high blood

sugar, cardiovascular diseases, arthritis and asthma were

not included in subsample analysis [71–83]. As expected,

our results show that compared to those without chronic

disease, individuals with chronic disease not directly

related to mental health were more likely to suffer from

depression. Therefore, even when chronic disease is not

considered to be a direct cause of depression in China,

chronic disease influences an individual’s susceptibility to

air pollution triggering depressive symptoms.

Given the above limitations, it is considered the results

of this study can be used when establishing air pollution

policies and policies related to both individual and public

health concerns. In this respect, the adverse effects of air

pollution on mental health should firstly be considered

when establishing air pollution guidelines. If the unfa-

vorable impacts of air pollution on mental health are over-

looked, the national emission standard levels may be higher

than optimal, which could result in excessive amounts of air

pollutants being emitted. In addition, our findings provide

a justification for establishing mental health interventions

that target air pollution exposure. Public health policies

should provide vulnerable people with information to help

them cope with the adverse effects of air pollution. Fur-

thermore, respondents with chronic disease found that

depressive symptoms were particularly susceptible to fluc-

tuations in air quality. Although all individuals are poten-

tially exposed to ambient pollution, the evidence suggests that

being of sound physical health cushions the depressive

symptom effects of air pollution exposure. As such, bas- ing

policies on effects observed in the general population may

be insufficient to protect vulnerable subgroups. The Chinese

government needs to enhance and focus preven- tion

strategies for those with chronic disease. For example, the Air

Quality Health Index could provide different public advice

for those with an elevated risk due to chronic disease when

providing a summary of the air quality and advice to prevent

adverse health effects [85].

Conclusion This study evaluated the association between air pollution

and depressive symptoms and was based on nationally

representative data relating to the Chinese middle- and

old-aged population. The role of chronic disease with

respect to air pollution and depressive symptoms was

also estimated. Air pollution was found to be an impor-

tant determinant of depressive symptoms, particularly

for those with chronic disease. When an individual had a

chronic disease, they were more vulnerable to the depres-

sive symptoms effects of air pollution than those with-

out chronic disease. Therefore, the adverse health effects

of air pollution should be taken into consideration while

establishing environmental and public health policies.

Abbreviations ANOVA: Analysis of Variance; CES-D: Center for Epidemiologic Studies Depres-

sion; CHARLS: China Health and Retirement Longitudinal Study; GDP: gross

domestic product; NCMS: the rural new cooperative medical scheme; NO2:

nitrogen dioxide; PM10: particle size smaller than 10 μm; PM2.5: particle size smaller than 2.5 μm; SO2: sulfur dioxide; TSP: total suspended particulates

(particle size smaller than100 μm); UEBMI: the urban employee-based basic medical insurance scheme; URBMI: the urban resident-based basic medical insurance scheme; WHO: World Health Organization.

Authors’ contributions QW, ZY conceived the paper. QW wrote the paper, ZY analyzed and inter-

preted the data. Both authors read and approved the final manuscript.

Author details 1 School of Business, Dalian University of Technology, Panjin 124221, Liaoning,

China. 2 Donlinks School of Economics and Management, University of Sci-

ence and Technology Beijing, Beijing 100083, China.

Acknowledgements We would like to acknowledge the China Health and Retirement Longitudinal

Study team for providing data and the training of using the dataset.

Competing interests The authors declare that they have no competing interests.

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 11 of 12

×

×

Consent for publication All of the authors have read and approved the paper and it has not been pub-

lished previously nor is it being considered by any other peer-reviewed journal.

Ethics approval and consent to participate The study was approved by the Institutional Review Board of Peking University

with ethical approval NO. (IRB00001052-11014).

Funding This paper was supported by the National Natural Science Foundation of

China (Nos. 71503059; 71673022; 71420107023); the Fundamental Research

Funds for the Central Universities [Nos. FRF-TP-16-050A1; FRF-BR-17-005B;

DUT17RC(4)24]; Dalian Social Science Foundation (No. 2016dlskyb004); Beijing

Social Science Foundation (No. 17LJB004).

Appendix See Table 4.

Table 4 Association of air pollution emission per capita/per unit area and depressive symptoms and the role of chronic disease: Robustness test

Variables Influence of SO2 emission intensity on depressive symptoms

Influence of TSP emission intensity on depressive symptoms

Model 1 Model 2 Model 3 Model 4

Marginal effects Boot. Std. Marginal effects Boot. Std. Marginal effects Boot. Std. Marginal effects Boot. Std.

Association of air pollution emission per unit area (tons per square kilometer) and depressive symptoms and the role of chron ic disease among respondents without mental-related chronic disease: Tobit model (N = 10,508)

Log of SO2 emission per unit area

Log of TSP emission per unit area

0.191 0.163 0.156 0.153 – – – –

– – – – 0.490*** 0.132 0.419*** 0.161

Chronic disease – – 0.856*** 0.107 – – 0.861*** 0.094

Log of SO2 emis- sion per unit

disease

– – 0.161* 0.093 – – – –

Association of air pollution emission per capita (tons per 10 thousand people) and depressive symptoms and the role of chroni c disease among

respondents without mental-related chronic disease: Tobit model (N = 10,508)

Log of SO2 emission per capita

Log of TSP per capita

0.359*** 0.122 0.332** 0.137 – – – –

Chronic disease – – 0.860*** 0.114 – – 0.865*** 0.088

Log of SO2 per capita chronic disease

Log of TSP per capita chronic disease

Sigma(u)

Sigma(e)

Log likelihood − 29,374 − 29,329 − 29,365 − 29,319

Models 1–8 are estimated using the xi:xttobit-command in Stata 14. Decentralization was calculated using the center-command, and Marginal effects was calculated using the margins-command

Control variables included individual’s demographic, socioeconomic status, health behaviors and city-level climate variables in living areas. City dummy variables

emission – – – – 0.615*** 0.121 0.564*** 0.122

emission – – 0.145 0.105 – – – –

emission – – – – – – 0.211* 0.118

2.089*** 0.072 2.041*** 0.057 2.093*** 0.081 2.046*** 0.076

3.452*** 0.041 3.455*** 0.034 3.447*** 0.048 3.449*** 0.037

area × chronic

Log of TSP emis- – – – – – – 0.284** 0.124 sion per unit area × chronic

disease

Sigma(u) 2.088*** 0.063 2.040*** 0.079 2.092*** 0.057 2.043*** 0.072

Sigma(e) 3.453*** 0.042 3.456*** 0.040 3.449*** 0.037 3.451*** 0.040

Log likelihood − 29,375 − 29,329 − 29,368 − 29,320

Model 5 Model 6 Model 7 Model 8

Marginal effects Boot. Std. Marginal effects Boot. Std. Marginal effects Boot. Std. Marginal effects Boot. Std.

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 12 of 12 were also controlled

* P < 0.10; ** P < 0.05; *** P < 0.01

Wang and Yang Int J Ment Health Syst (2018) 12:33 Page 13 of 12

− −

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub-

lished maps and institutional affiliations.

Received: 20 December 2017 Accepted: 11 June 2018

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