The superposition effects of air pollution on government health expenditure in China— spatial evidence from GeoDetector

Abstract

Background
As the fifth-largest global mortality risk factor, air pollution has caused nearly one-tenth of the world’s deaths, with a death toll of 5 million. 21% of China’s disease burden was related to environmental pollution, which is 8% higher than the US. Air pollution will increase the demand and utilisation of Chinese residents’ health services, thereby placing a greater economic burden on the government. This study reveals the spatial impact of socioeconomic, health, policy and population factors combined with environmental factors on government health expenditure.

Methods
Spearman’s correlation coefficient and GeoDetector were used to identify the determinants of government health expenditure. The GeoDetector consist of four detectors: factor detection, interaction detection, risk detection, and ecological detection. One hundred sixty-nine prefecture-level cities in China are studied. The data sources are the 2017 data from China’s Economic and Social Big Data Research Platform and WorldPOP gridded population datasets.

Results
It is found that industrial sulfur dioxide attributed to government health expenditure, whose q value (explanatory power of X to Y) is 0.5283. The interaction between air pollution factors and other factors will increase the impact on government health expenditure, the interaction value (explanatory power of × 1∩× 2 to Y) of GDP and industrial sulfur dioxide the largest, whose values is 0.9593. There are 96 simple high-risk areas in these 169 areas, but there are still high-risk areas affected by multiple factors.

Conclusion
First, multiple factors influence the spatial heterogeneity of government health expenditure. Second, health and socio-economic factors are still the dominant factors leading to increased government health expenditure. Third, air pollution does have an important impact on government health expenditure. As a catalytic factor, combining with other factors, it will strengthen their impact on government health expenditure. Finally, an integrated approach should be adopted to synergisticly governance the high-risk areas with multi-risk factors.

Full text

Xiaetal. BMC Public Health (2022) 22:1411
https://doi.org/10.1186/s12889-022-13702-y
RESEARCH
The superposition eects ofair pollution
ongovernment health expenditure inChina—
spatial evidence fromGeoDetector
Qi Xia1,2†, Xiyu Zhang1,2†, Yanmin Hu3†, Wanxin Tian1,2, Wenqing Miao1,2†, Bing Wu1,2, Yongqiang Lai1,2,
Jia Meng4, Zhixin Fan1,2, Chenxi Zhang1,2, Ling Xin1,2, Jingying Miao1,2, Qunhong Wu2,5, Mingli Jiao1,2,
Linghan Shan2,5, Nianshi Wang6, Baoguo Shi7* and Ye Li1,2*
Abstract
Background: As the fifth-largest global mortality risk factor, air pollution has caused nearly one-tenth of the
world’s deaths, with a death toll of 5 million. 21% of China’s disease burden was related to environmental pollution,
which is 8% higher than the US. Air pollution will increase the demand and utilisation of Chinese residents’ health
services, thereby placing a greater economic burden on the government. This study reveals the spatial impact of
socioeconomic, health, policy and population factors combined with environmental factors on government health
expenditure.
Methods: Spearman’s correlation coefficient and GeoDetector were used to identify the determinants of govern-
ment health expenditure. The GeoDetector consist of four detectors: factor detection, interaction detection, risk
detection, and ecological detection. One hundred sixty-nine prefecture-level cities in China are studied. The data
sources are the 2017 data from China’s Economic and Social Big Data Research Platform and WorldPOP gridded popu-
lation datasets.
Results: It is found that industrial sulfur dioxide attributed to government health expenditure, whose q value
(explanatory power of X to Y) is 0.5283. The interaction between air pollution factors and other factors will increase
the impact on government health expenditure, the interaction value (explanatory power of × 1∩× 2 to Y) of GDP and
industrial sulfur dioxide the largest, whose values is 0.9593. There are 96 simple high-risk areas in these 169 areas, but
there are still high-risk areas affected by multiple factors.
Conclusion: First, multiple factors influence the spatial heterogeneity of government health expenditure. Second,
health and socio-economic factors are still the dominant factors leading to increased government health expendi-
ture. Third, air pollution does have an important impact on government health expenditure. As a catalytic factor,
© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Open Access
†Ye Li is the first corresponding author and Baoguo Shi is the second
corresponding author. Qi Xia, Xiyu Zhang, Yanmin Hu and Wenqing Miao
contributed equally to this work.
*Correspondence: liye8459@163.com; bgshi2008@hotmail.com
1 Health Policy and Hospital Management Research Center, School of Health
Management, Harbin Medical University, Harbin 150086, Heilongjiang, China
7 Department of Economics, School of Economics, Minzu University
of China, No.27 Zhongguancun South Avenue, Beijing 100081, China
Full list of author information is available at the end of the article
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Background
As the fifth-largest global mortality risk factor, air pollu-
tion has caused nearly one-tenth of the world’s deaths,
with a death toll of 5 million [1]. Some scholars have
shown that every 10,000 tons of industrial sulfur dioxide
emissions in cities will lead to an increase in lung can-
cer and respiratory disease deaths by 0.035 and 0.030
per 10,000 people, respectively [2]. e total number of
premature deaths due to PM2.5-exposure across China
in 2013 reached 1.37 million [3] and predicted that the
number of deaths could reach 2.3 million by 2030 [4].
e World Health Organization has preliminarily esti-
mated that 21% of China’s disease burden was related to
environmental pollution, which is 8% higher than that
of the United States. Moreover, for every 1% increase in
PM2.5, household health care expenditure will increase
by 2.942% [5]. is would exacerbate an already-prob-
lematic situation, given that the total medical expenses of
clinic visits for respiratory diseases in China had already
reached an estimated 17.2–57 billion Yuan in 2014 [6].
As such, it is not difficult to see that air pollution will
increase the demand and utilisation of Chinese residents’
health services, thereby placing a greater economic bur-
den on the government.
Existing studies have proved that environmental,
socioeconomic, health and other factors are affecting
government health expenditure to varying degrees.
First, industrial sulfur dioxide has been considered a
representative air pollutant by the Asian Development
Bank in terms of environmental factors. e impact
of sulfur dioxide (SO2) on human beings has been
fully proved – long-term inhalation of SO2 can cause
chronic bronchitis, chronic rhinitis, and other diseases
[7]. Moreover, adverse weather factors have increased
the risk of disease – for example, the population is at
a higher risk of disease in the year of drought, leading
to increased health expenditure by between 9 and 17%
of total consumption [8]. Extreme high temperatures
will increase the number of inpatients and deaths, fur-
ther affecting the government’s health expenditure [9].
Second, socioeconomic factors will also have an impact
upon health expenditure – for example, certain studies
have shown that with every 1% increase in per capita
gross domestic product (GDP), health expenditure will
increase by 0.332% [10]. Furthermore, a 1% increase in
the level of urbanisation will lead to a 0.378% increase
in government health expenditure within the affected
region [11]. In addition, from 2008 to 2017, the age-
ing problem was increasingly serious; at this stage, the
share of government health expenditure increased from
5.7 to 7.5% [12, 13]. ird, health factors have a natural
driving effect on government health expenditure. With
every 1% increase in the number of beds, the health
expenditure will increase by 0.264% [11]. In sum, social,
health, policy, and environmental factors all impact
government health expenditure to varying degrees.
However, most of the existing literature is limited
to the impact of a single dimension on government
health expenditure [14, 15]. Few studies have exam-
ined the influence of air pollution on the government
health expenditure – particularly from a multi-dimen-
sional perspective via the superimposition of air pol-
lution with social, health, environmental, and policy
factors. Moreover, research regarding the spatial dif-
ferentiation between air pollution and government
health expenditure is still relatively nascent. And only
few articles focused on the spatial difference of health
expenditure; in China as caused by air pollution, albeit
at the provincial level [11].
Based on the above hypothesis, this study verified
spatial heterogeneity of various factors and their cou-
pling on government health expenditure from the
perspective of multi-dimensional factors. As such, we
have tried to address the gap in the body of research
regarding these topics. us, our study contributed to
the existing literature in two aspects. First, we intro-
duced a new method – the GeoDetector – to analyse
the spatial heterogeneity of government health expend-
iture, and its driving factors, in Chinese prefecture-
level cities. e method’s advantage is that it allows
for identifying spatial similarities between dependent
variables and independent variables and even allows
for detecting an interaction between driving factors.
e method’s q-value statistics is used to describe the
extent to which independent variables can account for
dependent variables and, thus, carries an exact physi-
cal meaning with no linear hypothesis. Second, envi-
ronmental factors were introduced into the model in
our study. Little attention has been paid to the impact
of socioeconomic, health, environmental and policy
factors on governments’ health expenditure in China
at prefecture-city level. Considering a number of pos-
sible known factors, our study quantified the impact on
health expenditure of prefecture-level cities in China.
combining with other factors, it will strengthen their impact on government health expenditure. Finally, an integrated
approach should be adopted to synergisticly governance the high-risk areas with multi-risk factors.
Keywords: Air pollution, Government health expenditure, GeoDetector
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Methods
Data source andvariable screening
Based on previous studies, we constructed a model of the
impact of air pollution on government health expendi-
ture, using the latter as the dependent variable. Govern-
ment health expenditure concerns governments’ funds
at all levels for health services, medical security subsi-
dies, health security administration and other health-
related undertakings. erefore, using government
health expenditure as a dependent variable can lead to
a more comprehensive evaluation of the government’s
investment in health. is study focused on the impact
of air pollution on government health expenditure, and
whether the impact of socioeconomic, health and policy
factors on government health expenditure has changed
under the superposition of air pollution factors.
According to the “China Statistical Yearbook – 2018”,
there are 294 prefecture-level cities, and 4 municipalities,
directly under the purview of the central government.
Due to a lack of data availability for many of these cit-
ies, the data for 200 prefecture-level cities and 4 munici-
palities were collected finally. e indexed data were
mainly collected via China’s Economic and Social Big
Data Research Platform, including GDP, urbanisation
level (UL), proportion of secondary industry (PSI), the
number of hospital beds (NHB), the number of hospitals
(NH), the number of (assistant) doctors (ND), integrating
medical insurance reform (IURMI), the proportion of
government health expenditure in GDP (PGH), annual
average temperature (AT), annual rainfall (AR), indus-
trial sulfur dioxide emissions (ISDE) and population den-
sity (PD) (Table1). It should be noted that PD was taken
from WorldPOP gridded population datasets and further
corrected according to yearbook demographic data. is
population remote-sensing dataset has been widely used
to estimate the spatial distribution of the population, as
can be found in much of the literature [16].
Spearman’s correlation coecient
Spearman’s correlation coefficient is used to measure the
dependency of two variables by quantifying the relation-
ship between government health expenditure and related
influencing factors, thereby determining whether the
relationship is positive or negative. e method uses a
monotone equation to evaluate the correlation between
two statistical variables. In this study, we used a bivariate
association analysis of bilateral tests. e formula for the
correlation coefficient, ρ, is as follows:
In this instance, the value ρ represents the associa-
tion between government health expenditure and each
(1)
ρ
=i(xi−x)yi−y
i
(xi−x)2
i
yi−y
2
Table 1 Descriptions of the indicators for influencing factors
Respects Variable Code Unit Data sources
Dependent variable Government health expenditure GHE 104 Yuan the Statistical Yearbook of the prefecture-level
cities in 2017
Socioeconomic factors Gross Domestic Product GDP 108 Yuan the Statistical Yearbook of the prefecture-level
cities in 2017
Urbanisation level UL Percent the Statistical Yearbook of the prefecture-level
cities in 2017
Proportion of Secondary Industry PSI Percent China Urban Statistical Yearbook – 2018
Health factors Number of Hospital Beds NHB Beds China Urban Statistical Yearbook – 2018
Number of hospitals NH Hospitals the Statistical Yearbook of the prefecture-level
cities in 2017
Number of doctors ND Person China Urban Statistical Yearbook – 2018
Policy factors Integration of urban and rural residents’ medical
insurance IURMI / Human resources and social security websites
of cities
Proportion of government health care expendi-
ture in GDP PGH Percent the Statistical Yearbook of the prefecture-level
cities in 2017
Environmental factors Annual average temperature AT Centigrade the Statistical Yearbook of the prefecture-level
cities in 2017
Annual rainfall AR Millimeter the Statistical Yearbook of the prefecture-level
cities in 2017
Industrial sulfur dioxide Emissions ISDE 104 Tons China Urban Statistical Yearbook – 2018
Population factor Population density PD 104 person
per square
kilometer
WorldPOP gridded population datasets
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Xiaetal. BMC Public Health (2022) 22:1411
influencing factor – with a range of [− 1, 1]. A positive
value indicates a positive correlation between two vari-
ables, whereas a negative value indicates a negative cor-
relation. Furthermore, larger values indicate stronger
correlations. e dependent variable, Y, represents
government health expenditure, while the independ-
ent variable, X, represents the influencing factor of
the GeoDetector. We used this method to evaluate the
dependence of government health expenditure on influ-
encing factors. e tool used to calculate the Spearman
correlation coefficient was IBM’s SPSS statistics package
(version 19).
The GeoDetector method (GDM)
In this study, the impact of 11 driving factors on Chi-
nese government health expenditure was measured via
the GeoDetector. GeoDetector is a spatial statistical
method for detecting spatial heterogeneity, quantifying
driving factors and their interactions. Its basic princi-
ple concerns the division of the study area into several
sub-regions. If the intra-layer variance is less than the
inter-layer variance, there will be spatial heterogeneity.
Compared with the traditional linear models, GeoDe-
tector can detect both qualitative and quantitative data
without considering the assumptions of either linear-
ity or collinearity. However, the detection of continu-
ous data needs to be translated into discrete qualitative
data – the difficulty lies in the discretisation of con-
tinuous data via the appropriate methods, which deter-
mines the discretisation method and interval range of
continuous data at different levels. en, factor detec-
tion and interaction detection were used to calculate
the q value and interaction q value respectively after
continuous discretisation data. By comparing the q
value and interactive q value of different levels of dis-
cretisation methods, the optimal discretisation method
is finally determined [17].
In this study, Jenks Natural Breaks Classification
method was used to classify the continuous data into
discrete categories. According to the interval value, the
10 numerical influencing factors were classified along
7 natural breakpoints, while the regions were arranged
in ascending order. e “1” sub-region is the mini-
mum interval value, whereas the “7” sub-region is the
maximum interval value. In addition, Sun adopted the
standard of 10 * 10 km [18]. Further since GeoDetec-
tor software can accommodate 32,767 at most [19],
we finally adopted 20 * 20 km areas. ArcGIS 10.2 was
used to delimit the administrative regions of China in
20 km*20 km areas. Subsequently, information regard-
ing the independent and dependent variables of each
grid point’s location was removed to make the variable
information of the grid point. ese variables were input
into GeoDetector.
GeoDetector consists of four detectors: factor detec-
tion, interaction detection, risk detection, and ecologi-
cal detection [19].
(1) Factor detection is used to detect the degree of expla-
nation of driving factors for spatial differentiation of
government health expenditure. e use of q allows
for the value to be measured, whose expression is:
Where: h = 1…; L = the Strata of government health
expenditure, or impact factor X; Nh and N are layer h
and the number of units in the whole region, respec-
tively; and σ2 are the variance of government health
expenditure of layer h and the district, respectively.
SSW and SST are, respectively, the sum of intra-layer
variances and the total variance of the whole region. e
range of q is [0, 1], which means that the influencing
factor has q% explanatory power concerning govern-
ment health expenditure. e larger the q value is, the
stronger the impact of the influencing factor on govern-
ment health expenditure will be. e value of q further
represents the influencing factor x, which explains gov-
ernment health expenditure, y, of 100 × q %.
(2) Interaction detection evaluates whether the inter-
active effect of different factors × 1 and × 2 will
increase or decrease the explanatory power of
government health expenditure. By comparing
the relationships among q(× 1∩× 2), q(× 1), and
q(× 2), the interaction value means whether the
interactive effect of different factors X1 and X2
will increase or decrease the explanatory power of
government health expenditure. The interaction
results can be divided into five categories: nonlin-
ear weaken, single-factor nonlinear weaken, two-
factor enhancement, independence, and nonlinear
enhancement (See Table2). The interaction rela-
tionship is as follows:
(3) For risk detection, according to the classification of
each influencing factor, the study area is divided
into multiple sub-regions to identify significant dif-
(2)
q
=1−
L
h=1Nhσ
2
h
Nσ
2=1−
SSW
SST
(3)
SSW
=
L
h=1
Nhσ2
h,SST =Nσ
2
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Xiaetal. BMC Public Health (2022) 22:1411
ferences in average government health expenditure
among the sub-regions. e formula is defined as:
Where
Yh
represents the average value of Y in the sub-
region (h); is the number of samples in the sub-region
(h), and Var is variance.
(4) Ecological detection determines whether the two
influencing factors have significant differences in the
spatial distribution of government health expendi-
ture and is expressed as:
Where, NX1 and NX2 represent the sample numbers of
two factors (× 1 and × 2), respectively. SSWX1 and SSWX2
are the sum of squares of the sub-regions as generated
by the factors X1 and X2, respectively. L1 and L2 repre-
sent the number of subregions of X1 and X2, respectively.
e null hypothesis is defined as h0 : SSWX1 = SSWX2. e
rejected h0 at the significance level α indicates that it is
statistically significant.
Natural breaks classication method
e GeoDetector requires that continuous data be trans-
formed into discrete data. Jenks Natural Breaks Classifi-
cation was used as the classification method to optimise
the layout of continuous data into “natural” categories.
e basic idea of natural breaks (Jenks) is to minimise
each class’s average deviation from the class’ means, and
maximise each class’ deviation from the means of the
(4)
t
yh=1yh2=Yh=1−Yh=2

Var(Yh=1)
η
h
=1
+Var(Yh=2)
η
h
=2

1/2
(5)
F
=
N
X1
(N
X2−
1)SSW
X
1
NX2(NX1
−
1)SSW X2
(6)
SSW
X=
L1
h=1
Nhσ2
h,SST X2=
L2
h=1
Nhσ
2
h
other group. In other words, the method seeks to reduce
the intra-class variance while maximising inter-class vari-
ance [20]. To determine the optimal classification, the
Jenks Natural Breaks Classification method was used to
determine the classification threshold. Because medical
insurance data have been divided into two categories –
“not implementing integrated medical insurance (1)” and
“implementing integrated medical insurance (2)” – we
used ArcGIS 10.2 software to classify the remaining 10
influencing factors used in this paper into 7 categories
via the Jenks Natural Breaks Classification method. e
regions were arranged in ascending order according to
the interval value; the “1″ sub-area is the minimum inter-
val value, while the “7″ sub-area is the maximum interval
value – as shown in the Fig.1.
Results
Spearman analysis
Spearman’s correlation coefficient was used to explore
the correlation between 12 influencing factors and gov-
ernment health expenditure – including socioeconomic,
health, policy, environmental and population factors, and
to detect if the deter relationship between was positive or
negative. e results showed that 6 of the 12 influencing
factors were significantly and positively correlated with
government health expenditure at p < 0.01, with influ-
encing factors registering a p < 0.05. e key factors of
each dimension are the number of doctors (0.784), GDP
for socioeconomic factors (0.719), IURMI for policy fac-
tors (0.344), PD for population factors (0.318) and ISDE
for environmental factors (0.243). e industrial sulfur
dioxide emission (ISDE) of the environmental factors has
a significant positive correlation (ρ = 0.243) with govern-
ment health expenditure (See Table3).
Factor detection analysis
e explanatory power (the q statistics) and the P value
(as obtained via factor detection) are shown in Table4.
e P values of 11 influencing factors were all less than
0.01, indicating that the 11 influencing factors were sta-
tistically significant. e results showed that the socio-
economic, health, policy, and environmental factors
of different regions impacted on government health
expenditure. Among these, the top 5 key factors affect-
ing the explanation of government health expenditure
were GDP (0.8999), NHB (0.8370), ND (0.8362), NH
(0.7502) and ISDE (0.5283). First, we further found
that the explanatory power of GDP, NHB, and ND
accounted for more than 80%, while NH exceeded 70%,
indicating that the level of economic development
and health resources available are key factors affecting
government health expenditure. Cities with relatively
developed economies and sufficient health resources
Table 2 Types of interaction between two factors on dependent
variables
Description Interaction
q(× 1∩× 2) < Min(q(x), q(× 2)) Nonlinear weakening
Min(q(× 1), q(× 2)) < q(× 1∩× 2) < M
ax(q(× 1), q(× 2)) Single factor nonlinear weakening
q(x1∩x2) > Max(q(× 1), q(× 2)) Two factor enhancement
q(×1∩×2) = q(× 1) + q(× 2) Independence
q(x1∩x2) > q(× 1) + q(× 2) Nonlinear enhancement
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Fig. 1 Spatial distributions of government health expenditure and influencing factors
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contributed more government health expenditure.
Second, the explanatory power of ISDE was more
than 50%, indicating that it had a significant impact
on government health expenditure, raising a warning
which should not be ignored. e explanatory power of
UL, PSI, AT, and AR was more than 10%, which indi-
cates that AT and AR are key factors affecting govern-
ment health expenditure. However, IURMI and PGH
accounted for more than 5%, which, in turn, shows that
IURMI and PGH will also significantly impact gov-
ernment health expenditure. It is noteworthy that the
impact is also minimal, indicating that the health poli-
cies of prefecture-level cities in China are fair and rea-
sonable. ere is little difference across spatial units
(See Table4).
Interaction detection
e P values of 12 influencing factors were all less than
0.01, indicating statistical significance. erefore, we
used the interaction detection to study the explana-
tory power of the factors above on government health
expenditure. e results showed that there are 66 pairs
of interaction combinations among the 12 influencing
factors – that is to say, the explanatory power of interac-
tion between any two factors is stronger than that of any
single factor. As such, some of these factors are nonlin-
ear enhanced after interaction (expressed as #), which is
the joint effect of the two factors is stronger than the sum
of their independent explanatory power. For example, q
(UL∩ISDE) 0.7543 > q (UL) 0.2119 + q (ISDE) 0.5283.
However, more interaction combinations between some
factors have a double-factor relationship (expressed as
*), which indicates that the joint effect of the two factors
is stronger than the maximum explanation of the two
factors when independent of one another. For example,
Table 3 The influencing factors and Spearman’s rho results of
government health expenditure
** When the condence level (double test) is 0.01, the correlation is signicant
* When the condence level (double test) is 0.05, the correlation is signicant
Respects Variable ρ
Socioeconomic GDP 0.719**
UL 0.125
PSI −0.136
Health NHB 0.775**
NH 0.632**
ND 0.784**
Policy IURMI 0.344**
PGH 0.046
Environment AT 0.171**
AR 0.111
ISDE 0.243*
Population PD 0.318**
Table 4 The q statistics of driving factors on government health
expenditure
Respects Variable q
Socioeconomic GDP 0.8999
UL 0.2119
PSI 0.1034
Health NHB 0.8370
NH 0.7502
ND 0.8362
Policy IURMI 0.0277
PGH 0.0494
Environmental AT 0.1537
AR 0.1350
ISDE 0.5283
Population PD 0.2769
Table 5 Interaction detection
a For double factor enhancement, q (X1 ∩ X2) > max (q (× 1), q (× 2))
b For nonlinear enhancement, q (X1 ∩ X2) > q (X1) + q (X2)
GDP UL PSI NHB ND NH IURMI PGH AT AR ISDE PD
GDP 0.8999
UL 0.9212a0.2119
PSI 0.9464a0.5826b0.1034
NHB 0.9628a0.9629a0.8616a0.8370
ND 0.9609a0.9656a0.8634a0.8473a0.8362
NH 0.946a0.9033a0.8584b0.8639a0.8668a0.7502
IURMI 0.9144a0.2940b0.1407b0.8581a0.8533a0.8479b0.0277
PGH 0.9839b0.6376b0.4440b0.9606b0.9678b0.8792b0.1330b0.0494
AT 0.9282a0.6373b0.5627b0.9154a0.9138a0.8996a0.2245b0.4657b0.1537
AR 0.9662a0.6200b0.4228b0.9585a0.9583a0.8968b0.1978b0.5646b0.3753b0.1350
ISDE 0.9593a0.7543b0.6296a0.9022a0.9075a0.862a0.5387a0.6697b0.6745a0.6646b0.5282
PD 0.9184b0.5916b0.5508b0.8747a0.8713a0.8552a0.4606b0.7951b0.6318b0.6112b0.8815b0.2769
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q (ISDE∩GDP) 0.9593 > q (GDP) 0.8999 > q (ISDE) 0.5283
(See Table5).
We focused on the interaction between industrial
sulfur dioxide and other factors. It was found that the
interaction value of GDP and ISDE is the largest, at q
(GDP∩ISDE) = 0.9593.
After the interaction between GDP (socioeconomic)
and NHB and ND (health), and ISDE (environmental),
their explanatory powers exceeded 90%, which showed
a double factor enhanced relationship. We also found
that, after interaction with ISDE, the q statistics of some
influencing factors increased by more than 50% when
compared with its own q statistics –including UL and
PSI (socioeconomic), IURMI, and PGH (policy), and
AT and AR (environmental) and PD (population). ISDE
has a significant impact on the improvement of explana-
tory powers when interacted with other factors. In addi-
tion, it is noteworthy that population factors have greatly
enhanced the driving force of all three health factors
(double factor enhancement), as shown in Fig.2.
Risk detection
rough the analysis of risk detection, the average val-
ues of government health expenditure across all the sub-
regions in terms of these 12 influencing factors were
obtained, and the differences among the sub-regions
Fig. 2 Original value q and interaction value with industrial sulfur dioxide emission
Fig. 3 The sub-regional government health situation across each factor
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of the influencing factors were pointed out. According
to the Jenks Natural Breaks Classification method, the
12 influencing factors were divided into 7 sub-regions
(in ascending order); the average value of government
health expenditure, which corresponds to each sub-
region, was calculated. For example, the average value
of government health expenditure across the seven
sub-regions in industrial sulfur dioxide was 354,972.2,
418,945, 384,154.3, 489,720.8, 439,745.7, 553,130.4, and
3,140,670. e results of the other factors were obtained
using the same method.
As shown in the statistical chart, the average govern-
ment health expenditure for each sub-region are on
the rise across GDP (socioeconomic), and NHB, NH,
ND, IURMI, and ISDE (environmental), as each factor
increases. By comparing the government health expendi-
ture for each factor within a sub-region, the sub-region
with the highest government health expenditure was
regarded as the highest-risk area. It was found that most
of the high-risk areas of influencing factors are located in
the seventh sub-region (See Fig.3).
We sorted the high-risk areas according to their socio-
economic, health, policy and environmental characteristics,
and summed up the 12 types of high-risk areas – namely,
socioeconomic high-risk areas (10), environmental high-risk
areas (12), policy high-risk areas (74), socioeconomic-health
high-risk areas (1), socioeconomic-environmental high-
risk areas (2), socioeconomic-policy high-risk areas (22),
socioeconomic-population high-risk areas (1), policy-envi-
ronment high-risk areas (31), socioeconomic-health-policy
high-risk areas (1), socioeconomic-policy-environment
high-risk areas (13), socioeconomic-health-policy-environ-
ment high-risk areas (1) and socioeconomic-health-policy-
environment-population high-risk areas (1).
For example, Xiamen belongs to the socioeconomic-
population-high-risk areas category due to the interac-
tion between UL, PSI and PD. Beijing belongs to the
socioeconomic-health high-risk area, due to the inter-
action of GDP and UL (socioeconomic), and NHB and
ND. Tangshan is affected by the joint actions of IURMI
(policy) and ISDE (environmental), categorising it as
a policy-environmental high-risk area. Chongqing is
a comprehensive high-risk area with a number of com-
bined factors, such as GDP and PSI (socioeconomic);
NHB, NH, ND, IURMI, and PGH (health); AT, AR, ISDE
(environmental), as shown in Fig.4.
Fig. 4 Distribution of high-risk areas
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Discussion
Based on GeoDetector with spatial consideration, this
study revealed the spatial impact of environmental fac-
tors alone, and the spatial impact of interaction between
environmental factors and other ones on government
health care expenditure. e following main conclusions
were obtained:
Air pollution isidentied toaect government healthcare
expenditure
e results of factor detection showed that industrial sul-
fur dioxide (environmental) accounted for 52.83% of gov-
ernment health expenditure, indicating that air pollution
was one of the core factors affecting government health
expenditure. e relationship between air pollution and
Fig. 5 Mechanism of air pollution on government health expenditure
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Xiaetal. BMC Public Health (2022) 22:1411
government health expenditure has been previously veri-
fied by a number of scholars and is consistent with our
findings [21].
Figure5 shows the impact mechanism of air pollution
and various factors on government health expenditure.
Air pollution has caused a wide range of threats to public
health, resulting in the surge in a number of diseases –
such as respiratory system, cardiovascular, and cerebro-
vascular diseases – thus promoting public demand for
increased health services [22–27]. e demand for health
services needs to be coordinated with the supply thereof
– resulting in health services’ actual utilisation. Via this
process, the corresponding improvement in health ser-
vice allocation, the implementation of health policies, or
the increase of health insurance costs (as caused by the
actual utilisation of health services) will lead to govern-
ment health expenditure across multiple dimensions.
First, government health expenditure is increased to
ensure the greater investment needed to meet residents’
growing demand and utilisation of health services – such
as the investment in health service allocation and health
insurance payments. In a study of urban workers in Tian-
jin, China, the proportion of hospitalisation expenses
for respiratory diseases accounts for more than 70% of
the total. In contrast, the proportion of non-individual,
out of pocket payments is 68.9% [28], indicating that
the health insurance system needs to bear a greater por-
tion of the expenses. Moreover, based on the perspective
of collaborative governance, more government health
expenditure should be used for health policies dealing
with the corresponding health problems caused by envi-
ronmental pollution. As for the impact of air pollution on
health expenditure, scholars have found that the spillover
effect is as much as half of the total effect, suggesting that
greater attention should be paid to the spatial correlation
between adjacent regions [2, 29]. As Chen has indicated,
if industrial sulfur dioxide emissions in a city increase
by 10,000 tons, the mortality rates from lung cancer and
respiratory diseases will grow by 0.217 and 1.543 per ten
thousand persons, respectively, in neighbouring areas.
Furthermore, we can also predict its impact on gov-
ernment health expenditure in surrounding areas. e
reduction of health expenditure caused by inter-regional
health reforms is eventually offset by air pollution.
Moreover, various factors will have an indirect and
superimposed influence on this process at various stages.
For example, there is a consensus that economic growth
promotes increased demand for health services and this
study tried to provide baisis for this causal chain com-
plementation. As shown in Fig. 5, public health is an
important factor in restricting the labour force, and both
GDP and industrial development need the labour force
as a support – that is, public health can further affect
economic development through its impact on the labour
force. In addition, one cannot ignore the guiding role of
policy on public behaviour, potentially having a profound
significance on the environment and the utilisation of
health services.
e relaxation of environmental regulations can pro-
mote regional economic development over a short
period, but the deteriorating ecological environment will
increase the burden of government health expenditure.
As mentioned at the 68th World Health Assembly, in
order to combat the health problems caused by envi-
ronmental pollution, it is necessary to widely publicise
healthy sector policies – such as Health in All Policies
(HiAP) – and cooperate in implementing communication
strategies at global, national and local levels, suggesting
greater policy activity and increased levels of govern-
ment health expenditure [30]. erefore, the governance
of air pollution, and its accompanying health problems,
requires cooperation between regions and departments.
However, although air pollution is easily spread, due to
the spatial effect thereof, it is feasible and particularly
important to control government expenditure.
The superposition ofair pollution factors andother factors
will increase theinuence ongovernment healthcare
expenditure
Although the contribution of air pollution to government
healthcare expenditure is large enough, it is even more
surprising that, when air pollution is combined with
other factors, the contribution will experience further
changes.
First, the results of the interaction detector show that
the combination of certain factors is stronger than the
sum of their single effects – that is to say, the combina-
tion of air pollution and another factor in the study will
produce a positive synergy effect. ese combinations
include the level of urbanization, PGH, PD and air pol-
lution. As shown throughout the existing literature, the
level of urbanisation is proven to be one of the catalysts
for air pollution [31–33]. Furthermore, the UL accounts
for 16.3% of the general expenditure on health [33]. e
increasing demand for health services, as brought about
by urbanisation, is bound to increase the government’s
healthcare investment profile. However, with the acceler-
ated urbanisation process, the problem of environmental
degradation has led to the need for additional govern-
ment investment in health services as a remedy [34, 35].
If the level of carbon dioxide emissions and the degree of
urbanisation increase by 1%, the need for health facili-
ties will increase by 0.037 and 0.327%, respectively [36]–
both of which would mean greater government health
expenditure. In addition, the concentration of the popu-
lation within cities leads to an uneven distribution of
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Xiaetal. BMC Public Health (2022) 22:1411
resources and an inconvenient transmission of resource
information [33], contributing to difficulties experienced
by residents in reaching needed health services. Further-
more, the inefficient integration of resources leads to the
waste of health services, which, in essence, wastes funds
allocated to expenditure on health. Unfortunately, when
serious air pollution occurs, the imbalance of resource
distribution between urban and rural areas is further
exacerbated, with the government needing to pay more
to account for the contradiction. Urbanisation is an
important influencing factor in the process of air pollu-
tion’s effect on government health expenditure. Whether
it is the worsening of the environment, the further expan-
sion of air pollution, an aggravation of the inefficiency
problem facing resource allocation and planning, and
the intensification of the relationship between demand
and supply, government health expenditure has deterio-
rated further, even exceeding the sum of the independent
effects had by air pollution and urbanisation.
Second, the combination of air pollution with one of
the factors under consideration results in a value greater
than the maximum value of the two factors indepen-
dently (that is, the interactive relationship is enhanced).
However, the contribution of this combination is weaker
than the simple addition of the two under the independ-
ent assumption. e above combinations include GDP,
NHB, NH, ND, IURMI, AT, AR, and air pollution. Here
we focused on the joint effect of urbanisation and air pol-
lution on government health expenditure. GDP has been
proven as being able to promote the growth of govern-
ment health expenditure [15]. Moreover, GDP improves
people’s living standards and greatly improves the utili-
sation of health services, especially in light of the nega-
tive effect of air pollution on health, thus generating an
increased demand for health expenditures and a greater
economic burden on the government – i.e., the growth
in GDP amplifies the negative effects had by air pollu-
tion on government health expenditure. In addition,
environmental factors (AT, AR) [37] have been proven to
have a direct effect on health and can even affect air pol-
lution (creating a positive feedback loop) [38]. It can be
seen from the formation of acid rain that industrial sul-
fur dioxide emissions in the air will pose a greater threat
to people’s health through rainwater [39, 40]. is could
potentially explain why environmental factors make air
pollution more important to public health and govern-
ment health expenditure. Factors related to the supply
of health services (NHB [41], NH, ND [42]) have also
been confirmed to have an impact on health expendi-
ture. ere is still a gap in the current demand for health
services, with the utilisation of health services unable to
fully meet demand [43]. However, the emergence of air
pollution increases the demand for health services and
intensifies the contradiction. erefore, more health ser-
vice facilities need to be established, and more govern-
ment health expenditure needs to be generated. Finally,
as an integration with health policy, health insurance
policies reduce the thresholds for residents to obtain
health insurance protection, and help promote fair access
to health services. Although air pollution increases the
demand for health services of residents, the health insur-
ance policy enables greater demands for affordable utili-
sation, which requires the government to increase health
expenditure and share the burden of the ill-health of resi-
dents. Nevertheless, the results of this study suggest that,
although these factors can increase air pollution’s weight
on government expenditure, the total effect is only
greater than either of them, which is not as good as UL
and PGH (as discussed in the previous paragraph).
Risk area detection andclassication
According to the results of risk area detection, a total
of 169 high-risk areas were found. Interestingly, there
are 96 simple high-risk areas. e number of high-risk
areas superimposed by mltiple factors is in the major-
ity. ese include socioeconomic-population high-risk
area (1), socioeconomic-health high-risk areas (1), soci-
oeconomic-policy high-risk areas (22), socioeconomic-
environmental high-risk areas (2), policy-environmental
high-risk areas (31), socioeconomic-policy-environmen-
tal high-risk areas (13), socioeconomic-health-policy
high-risk areas (1), socioeconomic-health-policy-envi-
ronmental high-risk areas (1), and socioeconomic-
health-policy-environmental-population high-risk areas
(1). To improve the cost-effectiveness ratio of govern-
ment health expenditure, different measures need to be
taken for different characteristics (according to the city),
rather than a large number of expenses that are repeat-
edly incurred to make up for the adverse health out-
comes caused by air pollution. First of all, for cities whose
government health expenditure is only restricted by air
pollution, the existing literature has proven that the air
pollution in this area is a serious concern and that there
is spatial spillover effect [29], which may be related to the
industrial belt located throughout the region. Although
industrial agglomeration areas provide employment
opportunities for residents and promote the development
of the local economy, it is evident that it also increases
the burdens related to health expenditure for local gov-
ernments. Governments should strengthen environmen-
tal infrastructure [44], implement policy control [44, 45]
and pay attention to the application of clean energy in
industrial production to reduce the burdens to healthcare
expenditure caused by air pollution.
For high-risk areas affected by multiple factors, it is
necessary to pay greater attention to the simultaneous
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Xiaetal. BMC Public Health (2022) 22:1411
effects of these factors’ conglomeration, not just air pol-
lution in isolation. Several studies have confirmed the
influence of social factors [46], health service factors
[47], environmental factors [48], policy factors [49] and
PD [50] on health services or healthcare expenditure
within the context of air pollution. Taking the high-risk
areas of social-health-environment-policy-population
(Shanghai) as example, serious air pollution inhibits the
development of the economy and, subsequently, reduces
the income levels of residents. However, this general
decline is more obvious among low-income groups. As
a result, the gap between the rich and the poor in vari-
ous air-pollution-afflicted regions is growing [51], leading
to different air pollution responses and other contrib-
uting factors. Ignoring such differences will render the
government’s actions meaningless. erefore, health
policy should be combined with environmental policy
and urban development planning [52]. In addition, it
would be helpful for further public health improvement
and government health expenditure control to reduce
air pollution sources, adopt intersectoral methods for
setting clear health benchmarks, targets and report-
ing mechanisms for air pollution detection and control
emerging clean energy technologies, and to treat air pol-
lution reduction as a health-related indicator in devel-
oping sustainable development policies [30]. A large
amount of government health expenditure could have
greatly improved public health. However, air pollution
has wasted these efforts and has, subsequently, increased
the government’s burden [11], especially under the con-
flict between supply and demand caused by high popula-
tion density. In the face of increasing health costs, a wide
range of joint measures between departments, such as
HiAP, can help improve the role played by government
health expenditure.
Conclusion
Using the data of China in 2017, we explored the influ-
encing factors of air pollution on government health
expenditure and its spatial governance by using GeoDe-
tector. e results show that air pollution is indeed the
explanatory factor of government health expenditure, but
in this process, UL, PGH, GDP, NHB, ND, NH, IURMI,
AT, AR and PD all increase this effect. In addition, in 200
prefecture-level cities and 4 municipalities, 169 regions
are at high risk. Interestingly, most risk areas are driven
by multiple factors. is also warns us at the policy level
that measures should be taken to suit local conditions in
different regions. In the areas only affected by air pollu-
tion, the government should strengthen the construction
of environmental infrastructure, implement policy con-
trol, and pay attention to the application of clean energy
in industrial production, so as to reduce the burden of
air pollution on medical and health expenditure. But in
the high-risk areas affected by multiple factors, we must
pay more attention to the simultaneous influence of these
factors; at the same time, a variety of joint measures, such
as HiAP, should be taken among various departments to
help improve the role of government health expenditure.
Limitations andprospect
First, we use yearly cross-sectional data of 2017 to ana-
lyze the spatial heterogeneity of government health
expenditure and its associated factors. Despite the use
of an appropriate methodology to avoid bias as much as
possible, some limitations of the cross-sectional data are
difficult to resolve completely. erefore, it is necessary
to implement corresponding spatiotemporal heterogene-
ity studies to provide stronger supporting evidence for
causality when data available. Second, this study is based
only on SO2 which was proved to have strong impacts
on health or health expenditure, ignoring other air pol-
lutants. We did not use all common air pollutants in
this study due to the current controversial methods for
estimating the effects of air-pollutant mixtures and the
poor availability of monitoring data for multiple air pol-
lutants. is may lead to an underestimation of air pol-
lution effects, which subsequent studies could attempt
to improve when more data available and methodologies
upgraded.
Acknowledgements
We acknowledge the outstanding contributions of Qi Xia, Xiyu Zhang, Yanmin
Hu and Wenqing Miao who all contributed equally to the first author of this
article.
Authors’ contributions
Qi Xia, Xiyu Zhang, Ye Li contributed to the conception of the manuscript
and wrote the manuscript. Wanxin Tian, Nianshi Wang, Zhixin Fan, Wenqing
Miao collected the materials. Bing Wu, Yongqiang Lai, Jia Meng, Chenxi Zhang,
Ling Xin, Jingying Miao collected the data. Qi Xia, Baoguo Shi, Linghan Shan
contributed to the analysis or interpretation of data. Yanmin Hu, Qunhong Wu
and Mingli Jiao were responsible for the guidance of revision opinions. The
author(s) read and approved the final manuscript.
Funding
This work was supported by the National Natural Science Foundation of China
[grant number [71874045, 71403073, 72174047], Natural Science Founda-
tion of Heilongjiang Province of China [grant number LH2021G015], China
Postdoctoral Science Foundation [grant number 2016M590296], Heilongjiang
Postdoctoral Science Foundation [grant number LBH-Z14166], Heilongjiang
Health and Family Planning Commission Project [grant number 2014–427],
MOE (Ministry of Education in China) Liberal arts, Social Sciences Foundation
(grant number 19YJCGAT004) and National Social Science Foundation of
China (grant number 20BGJ026).
Availability of data and materials
Dataset available from the China’s Economic and Social Big Data Research
Platform, https:// data. cnki. net/ Yearb ook/ Navi? type= type& code=A .
Declarations
Ethics approval and consent to practice
Not applicable.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 14 of 15
Xiaetal. BMC Public Health (2022) 22:1411
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Health Policy and Hospital Management Research Center, School of Health
Management, Harbin Medical University, Harbin 150086, Heilongjiang, China.
2 School of Public Health, Harbin Medical University, Harbin 150086, Heilongji-
ang, China. 3 Heilongjiang Provincial Hospital, Harbin 150086, Heilongjiang,
China. 4 The 2nd Affiliated Hospital of Harbin Medical University, Harbin,
150086, Heilongjiang, China. 5 Department of Social Medicine, Harbin Medical
University, Harbin 150086, Heilongjiang, China. 6 The Department of Hospital
Offices, the affiliated Wuxi No.2 People’s Hospital of Nanjing Medical Univer-
sity, Liangxi District, Wuxi 214002, Jiangsu, China. 7 Department of Economics,
School of Economics, Minzu University of China, No.27 Zhongguancun South
Avenue, Beijing 100081, China.
Received: 20 December 2021 Accepted: 22 June 2022
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