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healthcare
Article
The Impacts of Regional Regulatory Policies on the
Prevention and Control of Chronic Diseases in China:
A Mediation Analysis
Huihui Huangfu 1, 2, †, Qinwen Yu 1, 2, †, Peiwu Shi 2,3, Qunhong Shen 2,4, Zhaoyang Zhang 2,5, Zheng Chen 2,6,
Chuan Pu 2,7, Lingzhong Xu 2,8, Zhi Hu 2,9, Anning Ma 2, 10, Zhaohui Gong 2 ,11 , Tianqiang Xu 2,12, Panshi Wang 2, 13,
Hua Wang 2,14, Chao Hao 2,15, Qingyu Zhou 1,2, Li Li 1,2, Chengyue Li 1, 2, * and Mo Hao 1, 2, *
Citation: Huangfu, H.; Yu, Q.; Shi, P.;
Shen, Q.; Zhang, Z.; Chen, Z.; Pu, C.;
Xu, L.; Hu, Z.; Ma, A.; et al. The
Impacts of Regional Regulatory
Policies on the Prevention and
Control of Chronic Diseases in China:
A Mediation Analysis. Healthcare
2021,9, 1058. https://doi.org/
10.3390/healthcare9081058
Academic Editor:
Jose Granero-Molina
Received: 27 June 2021
Accepted: 15 August 2021
Published: 18 August 2021
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1Research Institute of Health Development Strategies, Fudan University, Shanghai 200032, China;
19111020043@fudan.edu.cn (H.H.); 19211020038@fudan.edu.cn (Q.Y.); zhouqingyu@fudan.edu.cn (Q.Z.);
yiran_eric@126.com (L.L.)
2Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai 200032,
China; pwshi@163.com (P.S.); shenqunhong108@163.com (Q.S.); zhangzhy@nhc.gov.cn (Z.Z.);
chenzhengjd@163.com (Z.C.); puchuan68@sina.com (C.P.); lzxu@sdu.edu.cn (L.X.); aywghz@126.com (Z.H.);
yxyman@126.com (A.M.); zhgong_zg@163.com (Z.G.); xtq1960@icloud.com (T.X.);
wangpanshi03@163.com (P.W.); jswstwh@163.com (H.W.); 18906113216@189.cn (C.H.)
3Zhejiang Academy of Medical Sciences, Hangzhou 310012, China
4School of Public Policy and Management, Tsinghua University, Beijing 100084, China
5Project Supervision Center of National Health Commission of the People’s Republic of China,
Beijing 100044, China
6
Department of Grassroots Public Health Management Group, Public Health Management Branch of Chinese
Preventive Medicine Association, Shanghai 201800, China
7School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
8School of Public Health, Shandong University, Jinan 250012, China
9School of Health Service Management, Anhui Medical University, Hefei 230032, China
10 School of Public Health, Jining Medical University, Jining 272067, China
11
Committee on Medicine and Health of Central Committee of China ZHI GONG PARTY, Beijing 100011, China
12 Institute of Inspection and Supervision, Shanghai Municipal Health Commission, Shanghai 200031, China
13 Shanghai Municipal Health Commission, Shanghai 200031, China
14 Jiangsu Preventive Medicine Association, Nanjing 210009, China
15 Changzhou Center for Disease Control and Prevention, Changzhou 213003, China
*Correspondence: lichengyue@fudan.edu.cn (C.L.); haomo03@fudan.edu.cn (M.H.);
Tel.: +86-21-33561022(C.L. & M.H.)
† Huihui Huangfu and Qinwen Yu are joint primary authors.
Abstract:
Regional regulatory policies (RPs) are a major factor in the prevention and control of
chronic diseases (PCCDs) through the implementation of various measures. This study aimed to
explore the impacts of RPs on PCCDs, with a focus on the mediating roles of community service.
The soundness of the regulatory mechanism (SORM) was used to measure the soundness of RPs
based on 1095 policy documents (updated as of 2015). Coverage provided by community service
institutions (CSIs) and community health centres (CHCs) was used to represent community service
coverage derived from the China Statistical Yearbook (2015), while the number of chronic diseases
(NCDs) was used to measure the effects of PCCDs based on data taken from the 2015 China Health
and Retirement Longitudinal Study survey. To assess the relationship between SORM, NCDs and
community service, a negative binomial regression model and mediation analysis with bootstrapping
were conducted. Results revealed that there was a negative correlation between SORM and NCDs.
CSIs had a major effect on the relationship between RPs and PCCDs, while CHCs had a partial
mediating effect. RPs can effectively prevent and control chronic diseases. Increased effort should
also be aimed at strengthening the roles of CSIs and CHCs.
Keywords:
regulatory policy; prevention and control of chronic diseases; community service institu-
tion; community health centre; mediation analysis
Healthcare 2021,9, 1058. https://doi.org/10.3390/healthcare9081058 https://www.mdpi.com/journal/healthcare
Healthcare 2021,9, 1058 2 of 14
1. Introduction
The prevalence of chronic noncommunicable diseases constitutes a threat to human
life, health, and sustainable development [
1
]. Nowadays, chronic diseases have become
the top risk factors for health, accounting for more than 80% of the 10.3 million annual
deaths and 68.6% of the overall economic burden in China [
2
]. In this context, several
factors have seriously threatened people’s health, thus affecting social harmony and the
credibility of the Chinese governments, specifically including low awareness rates, long
latency, high incidence rates, long clinical courses, high mortality rates, and low control
rates [
2
,
3
]. These issues demonstrate the need for increased attention on chronic disease,
particularly to reduce the risk of illness while improving the overall quality of life.
Chronic diseases are preventable and controllable. Interventions on the prevention
and control of chronic diseases (PCCDs) are known to aid in early detection, diagnosis, and
treatment initiatives while reducing related economic burdens [
4
]. Meanwhile, regulatory
policies (RPs) can improve the rate of service coverage by promoting the implementation
of prevention measures [
5
], thereby constituting a major source of control for PCCDs.
Extensive research has also shown that the soundness of RP has a significant impact on
PCCDs [
6
]. For example, Huang Jianming et al. [
7
] found that strong community RPs can be
used to improve several problematic issues among patients, including hypertension, while
Locke et al. [
8
] demonstrated that RPs had positive effects on the management of diabetic
patients in Canada, and Francisco et al. [
9
] proposed the implementation of multifunctional
services designed to monitor and manage COPD patients based on the effectiveness of a
chronic disease management model.
The PCCDs is a common and effective practice in many communities [
10
]. Specifically,
in China, community service institutions (CSIs) have become increasingly valuable for
PCCDs since the new health care reforms in 2009 [
11
,
12
]. CSIs are initiated by the govern-
ment and mainly responsible for public-welfare-based social service activities; the service
scope mainly includes medical care, pensions, and living [
13
]. Similarly, community health
centres (CHCs), which are a smaller unit of CSIs, provide medical and health services
and implement important strategies for PCCDs at the community level [
14
]; this was also
observed by Wong [
15
]. CHCs help to detect risk factors, provide health education, and
prevent chronic diseases [
16
], as has also been reported by Kim and Sharma [
17
,
18
]. There-
fore, CSIs and CHCs are important factors in promoting health and preventing chronic
diseases.
To improve PCCDs, it is necessary to understand the relationship between RPs, CSIs,
CHCs, and PCCDs. According to the Donabedian’s model, which contains structure,
process, outcome, and the relationship among them (structure-process-outcome) [
19
], RPs
(structure) influence the effects of PCCDs (outcome) through implementation at the process
level. CSIs and CHCs, as the ‘gatekeepers’ of community members and ‘vanguards’ of
disease prevention and control, provide social support and prevention services, which can
effectively control the spread and deterioration of chronic diseases (process) [
20
]. The two
can work together to implement the chronic disease control. We, therefore, assumed that
the relationship between RPs and PCCDs could be explained based on both the coverage of
CSIs and CHCs. Many studies have adopted mediation analysis. For instance, Vedanthan
et al. found that community health workers mediated hypertension care policy and the
control of blood pressure [
21
]. Chen et al. reported that social capital has an effect on
physical activity and nutrition [
22
]. However, no available study in the context of the
relationship between RPs and PCCDs and the pathways has adopted mediation analysis.
Therefore, this study aimed to fill this gap by exploring the association between RPs and
PCCDs, with a focus on whether CSIs and CHCs mediated this relationship.
Healthcare 2021,9, 1058 3 of 14
2. Materials and Methods
2.1. Study Design
The study setting included 31 provinces in China. A mediation analysis was conducted
to determine whether CSIs and CHCs mediated the relationship between RPs and PCCDs.
We, therefore, hypothesised the following (Figure 1):
Figure 1.
Mediating model for this study’s conceptual framework. Abbreviations: RPs, regulatory
policies; CSIs, community service institutions; CHCs, community health centres; PCCDs, prevention
and control of chronic diseases.
Hypothesis 1a (H1a).
The soundness of RPs has a positive impact on the coverage of CSIs (CHCs).
Hypothesis 1b (H1b). The coverage of CSIs (CHCs) has a positive impact on PCCDs.
Hypothesis 1c (H1c). The soundness of RPs has a positive impact on PCCDs.
2.2. Measurements
2.2.1. The Soundness of RPs
We constructed the soundness of the regulatory mechanism (SORM) to evaluate the
soundness of RPs. The SORM should have at least three characteristics: comprehensiveness,
authority, and implementation [
23
]. Here, the comprehensiveness of the mechanism
requires the coverage of essential elements in the policies for PCCDs and clearly outlined
responsibilities for each of the departments covered, based on the services they provide [
24
].
The authority of the mechanism requires issuance of documents by authorities (legislature,
government, health commission, etc.) to reflect the importance of RPs. Furthermore, the
mechanism requires supervision by external restraint mechanisms for the implementation
of the policies [
25
]. Therefore, four quantitative indicators were adopted. These were
named regulatory element coverage rate (RECR), departmental responsibilities clarity rate
(DPCR), regulatory mechanism authority rate (RMAR), and accountability mechanism
clarity rate (AMCR), and their definitions are detailed in Table 1[
23
]. Additionally, SORM
was measured based on the sum of the weights of the four quantitative indicators [
26
].
The study assumed that the higher the SORM (range from 0 to 100 percent), the better the
soundness of RPs [26].
Healthcare 2021,9, 1058 4 of 14
Table 1. Evaluation indicators for the soundness of RPs.
Characteristics Quantitative
Indicators Definition of Indicators
Comprehensiveness RECR (%)
The proportion of the number of regulatory
elements covered in a city’s chronic disease health
policy document collection to the 25 required
elements
DPCR (%)
The proportion of the number of departments with
clear and measurable responsibilities to the 22
departments that should be included in PCCDs
Authority RMAR (%)
The proportion of the number of authority of
government branches and regulatory mechanism
document sets to the total required
Implementation AMCR (%)
The proportion of the number of departments with
clearly defined monitoring agencies and
accountabilities to the 22 departments that should
be included in PCCDs
Abbreviations: RPs, regulatory policies; RECR, regulatory element coverage rate; DPCR, departmental responsi-
bilities clarity rate; RMAR, regulatory mechanism authority rate; AMCR, accountability mechanism clarity rate;
PCCDs, prevention and control of chronic diseases.
2.2.2. PCCDs
The number of chronic diseases (NCDs) was used to evaluate the effects of PCCDs. In
this regard, we defined chronic diseases as those with long durations and slow develop-
ment [
27
]. A total of 13 chronic diseases were considered according to the standards of data
collection presented in the China Health and Retirement Longitudinal Study (CHARLS),
including heart disease, stroke, malignant tumour, asthma, chronic obstructive pulmonary
disease, diabetes, hypertension, dyslipidaemia (WHO) [
28
], memory-related diseases (e.g.,
Alzheimer’s disease, brain atrophy, Parkinson’s disease), kidney disease (excluding tu-
mours and cancers), arthritis/rheumatism, liver disease (excluding fatty liver, tumours,
and cancers), and chronic gastroenteritis (ICD-11) [28].
2.2.3. The Coverage of CSIs and CHCs
The coverage of CSIs was measured using the coverage rate of the community service
institutions (CRCSI), while the coverage of CHCs was measured using the coverage rate of
the community health centres (CRCHC).
2.3. Data Collection
Data were collected from the quantitative analysis of policy contents (which con-
tributed to the soundness of RPs), CHARLS (contributed to PCCDs), and China Statistical
Yearbook (contributed to CSIs and CHCs).
We first obtained SORM data from policy contents (the coding template is shown in
Table A1). Here, we collected a range of policy documents, which were either publicly
available or extracted with permission [
29
] from official websites, including those run by
relevant government entities and public health agencies focused on chronic diseases in
China (updated as of 2015). The types of policy documents included laws, regulations,
plans, guidelines, and others, resulting in a total of 1095 documents from 31 provinces in
China.
This study’s researchers were previously trained and thus understood the standard-
ised methods for collecting necessary documents. The coding information mainly included
two components, the first of which consisted of basic information related to the documents,
including their official names, types, year of publication, and department or institution of
publication, while the second consisted of contents related to RPs aimed at chronic disease,
including content forms (e.g., long-term goals and short-term goals) [
25
], responsibilities,
work contents, tasks, assessment indicators, assessment requirements, and accountabil-
Healthcare 2021,9, 1058 5 of 14
ity. We analysed the credibility of the data collection process via the test-retest reliability
method with intraclass correlation coefficient (ICC). After two experienced researchers
conducted the retest, the ICC was found to be 0.997; as this was greater than 0.75, the data
collection process was of high credibility.
Next, NCDs data were drawn from the 2015 CHARLS survey and set as dependent
variables. CHARLS was a longitudinal survey that aimed to be representative of the
residents in mainland China aged 45 and older, with no upper age limit [
30
]. Multi-stage
stratified probability-proportional-to-size sampling was used to conduct the survey, which
randomly selected 150 counties/districts and 450 villages/resident committees, thereby
covering 19,000 individuals living in 12,400 households [
31
] from 28 provinces (data
from Hainan, Ningxia, and Tibet were missing), thus collecting a high-quality nationally
representative sample [
31
–
34
]. The CHARLS database includes a series of topics, such as
demographics, family structure/transfer, health status and functioning, health care and
insurance, work, retirement and pension, income and consumption, and community-level
information [
33
]. Subjects with missing or unreasonable responses were excluded from
analysis, thus resulting in a final sample size of 16,693.
Finally, CRCSI and CRCHC data were collected from the 2015 China Statistical Year-
book and set as mediating variables.
2.4. Statistical Analysis
Data were analysed using both EXCEL 2019 (Microsoft, Redmond, WA, USA) and
Stata 14.0 (Stata Corp., College Station, TX, USA).
To avoid an inherent reverse-causality issue, considering that chronic diseases are long
duration illnesses [
27
], we used past RPs to reflect the accumulation of chronic diseases
over a long timeframe. The soundness of RPs is a gradual improvement process; therefore,
we used the policy contents (updated as of 2015) to comprehensively reflect past regulatory
effects. As a result of the accessibility of NCDs, data from 28 provinces were used for
descriptive statistics and mediation analysis.
To determine the mediating roles of the CRCSI and CRCHC on the relationship
between RPs and PCCDs, we used Spearman’s correlation analysis to assess the multi-
collinearity between SORM, CRCHC, CRCSI, and NCDs. Multicollinearity meant that
highly correlated variables (r > 0.90) or mediation variables that were not correlated with
either SORM or NCDs were excluded from the mediation analyses [22].
Subsequently, the following negative binomial regression model was established to
analyse the relationship between RPs and PCCDs, as the outcome variable is a count
variable with over-dispersed distribution (alpha 95%CI (0.27, 0.31)).
ln(λi)=β0+cSORMi+δ0Xi+γ0Provincei+εi(1)
Number of chronic diseases (NCDs) was set as the dependent variable, and
λi
was the
expected count of NCDs.
SORMi
was set as the independent variable.
Xi
was used as a
control variable at the individual level, including age, gender, marital status, education
attainment, annual household income per capita, medical insurance, pension insurance,
smoking, and drinking.
Provincei
was used as a control variable at the provincial level,
including GDP per capita (reflective of the regional economic level), and the proportion of
the population over 65 years of age. εiwere the residuals.
Finally, we conducted a mediation analysis with bootstrapping using 5000 replica-
tions [
35
] and bias-corrected and accelerated confidence interval (BCa CI) [
36
] to examine
whether community service mediated the relationship between SORM and NCD. The me-
diation method required the following conditions: (1) SORM was significantly associated
with NCD (total effect; c coefficient), (2) SORM was significantly associated with CRCSI
(CRCHC) (a coefficient), (3) when controlling for SORM, CRCSI (CRCHC) was significantly
associated with NCD (b coefficient), (4) the relationship between SORM and NCD was
reduced (direct effect, c’ coefficient) when controlling for CRCSI (CRCHC) (indirect effect,
Healthcare 2021,9, 1058 6 of 14
a*b). The proportions mediated were determined by dividing the indirect effect (a*b) by
the total effect (c coefficient).
3. Results
3.1. Baseline Characteristics
Table 2shows the SORM of China and variables at the provincial level in 2015, as used
in this study. The SORM was 9.70%, thus indicating substantial room for improvement
in regard to the RPs aimed at chronic disease. DPCR was 4.92%, thus indicating that
responsibilities should be more clearly defined. The median value of AMCR was 0.55%,
thus indicating the lack of an external accountability mechanism in each department, which
restricted the effectiveness of PCCDs. The mean of CRCSI (58.46%) was higher than the
mean of CRCHC (6.72%).
Table 2. Characteristics of SORM and variables at the provincial level in 2015.
Indicators Mean Value/Median
Value SD/IQR
SORM (%) 9.70 3.67
RECR (%) 37.84 10.32
DPCR (%) 4.92 4.29
RMAR (%) 23.55 6.91
AMCR (%) 10.55 (0, 1.85)
Community service
CRCHC (%) 6.72 6.20
CRCSI (%) 58.46 54.00
Economic and aging level
GDP per capita (thousand yuan) 54.61 24.00
Proportion of population over 65 years of age (%) 10.35 1.70
1
The indicator was expressed as the median (IQR). Abbreviations: SORM, soundness of the regulatory mechanism;
RECR, regulatory element coverage rate; DPCR, departmental responsibilities clarity rate; RMAR, regulatory
mechanism authority rate; AMCR, accountability mechanism clarity rate; CRCHC, coverage rate of the commu-
nity health centres; CRCSI, coverage rate of the community service institutions; SD, standard deviation; IQR,
interquartile range.
Table 3shows baseline characteristics of the study variables. The mean participant
age was 61 years, and there was a slightly higher proportion of females (51.85% vs. 48.15%
males). A total of 27.36% of participants did not suffer from chronic disease. However,
more than 70% had at least one chronic disease, with some suffering from multiple chronic
diseases.
Healthcare 2021,9, 1058 7 of 14
Table 3. Variable descriptions (n= 16,693).
Variables Category Mean
(Median)/n SD (IQR)/%
NCDs 0 4567 27.36
1 4481 26.84
2 3379 20.24
3 2023 12.12
4 1162 6.96
≥5 1081 6.48
Control variables
Age (years) 61 10.01
Sex Male = 1 8037 48.15
Female = 2 8656 51.85
Registered residence Town = 1 3547 21.25
Rural = 0 13,146 78.75
Marital status Married = 1 13,349 79.97
Other = 0 3344 20.03
Education attainment Elementary school and below = 1 4900 29.35
Junior high school = 2 5665 33.94
Senior high school and above = 3 6128 36.71
Medical insurance Yes = 1 15,234 91.26
No = 0 1459 8.74
Pension insurance Yes = 1 14,696 88.04
No = 0 1997 11.96
Drinking Yes = 1 7733 46.33
No = 0 8960 53.68
Smoking Yes = 1 6912 41.41
No = 0 9781 58.59
Annual household income per capita(yuan)
16993.50 (2470, 17,000)
1
The indicator was expressed as the median (IQR). Abbreviations: NCDs, number of chronic diseases; SD, standard deviation; IQR,
interquartile range.
3.2. Correlation Analysis
Table 4summarises the relationships between variables using Spearman’s correlation
analysis. SORM was positively correlated with both CRCHC (r = 0.331, p< 0.01) and
CRCSI (r = 0.473, p< 0.01). Conversely, NCD exhibited significant negative correlations
with SORM (r =
−
0.029, p< 0.01), CRCHC (r =
−
0.049, p< 0.01), and CRCSI (r =
−
0.059,
p< 0.01).
Table 4.
Spearman’s correlation analysis on the relationships between SORM, CRCHC, CRCSI, and
NCD per capita.
Variables SORM CRCHC CRCSI
CRCHC 0.331 ***
CRCSI 0.473 *** 0.396 ***
NCD per capita −0.029 *** −0.049 *** −0.059 ***
*** p< 0.01. Abbreviations: SORM, soundness of the regulatory mechanism; CRCHC, coverage rate of the
community health centres; CRCSI, coverage rate of the community service institutions; NCD, number of chronic
diseases.
3.3. Regression Analysis
Table 5summarises the relationship between SORM and NCDs. SORM exhibited a
significant negative correlation with NCD (
β
=
−
0.014, p< 0.01). When controlling for
variables at the provincial level, we found that SORM was still significantly associated with
NCD (β=−0.010, p< 0.01). In this case, SORM had a positive effect on the NCD.
Healthcare 2021,9, 1058 8 of 14
Table 5. Negative binomial regression analysis of the effects of SORM on NCDs (per capita).
Variables Model 1 Model 2
βSE βSE
SORM −0.014 *** 0.002 −0.010 *** 0.003
Control variables (individual level)
Age 0.019 *** 0.001 0.019 *** 0.001
Sex 0.211 *** 0.023 0.213 *** 0.023
Registered residence 0.136 *** 0.019 0.139 *** (0.019)
Marital status 0.011 0.019 0.011 (0.019)
Education attainment
(Reference group:
elementary school and below)
Junior high school 0.082 *** 0.019 0.081 *** 0.019
Senior high school and above 0.028 0.022 0.031 0.022
Log Annual household income per capita −0.016 *** 0.004 −0.015 *** 0.005
Medical insurance 0.139 *** 0.028 0.136 *** 0.028
Pension insurance 0.049 * 0.025 0.045 * 0.025
Drinking 0.037 ** 0.017 0.028 * 0.017
Smoking 0.057 *** 0.021 0.065 *** 0.021
Control variables (provincial level)
Log GDP per capita −0.094 *** 0.026
Proportion of population over 65 years of age 0.028 *** 0.005
alpha 95% CI (0.270, 0.311) (0.267, 0.308)
*** p< 0.01, ** p< 0.05, * p< 0.1. Abbreviations: SORM, soundness of the regulatory mechanism; NCDs, number of chronic diseases.
3.4. Mediation Effects
Table 6shows the mediating effects of CRCHC and CRCSI. There was a negative
correlation between SORM and NCD (path: total effect,
β
=
−
0.014; 95% CI:
−
0.022,
−
0.006). Next, both CRCHC (path: indirect effect,
β
=
−
0.002; 95% CI:
−
0.003,
−
0.001) and
CRCSI (path: indirect effect,
β
=
−
0.006; 95% CI:
−
0.008,
−
0.005) mediated the relationship
between SORM and NCD. However, the bootstrap analysis showed that each played
different mediating roles. CRCHC had a partial mediating effect (11.31% of the total), with
the 95% CI of the direct effects in model B including the value of zero, while CRCSI had a
major mediating effect (45.42% of the total).
Table 6. Bootstrap analysis of the mediation effects.
Model Path βSE 95% CI Promotion
Mediated
Total effect −0.014 *** 0.004 −0.022, −0.006
A: CRCHC Direct effect −0.012 *** 0.004 −0.021, −0.004 11.31%
Indirect effect
−0.002 *** 0.001 −0.003, −0.001
B: CRCSI Direct effect −0.008 * 0.004 −0.016, 0.001 45.42%
Indirect effect
−0.006 *** 0.001 −0.008, −0.005
Bootstrapped standard errors in parentheses *** p< 0.01, * p< 0.1. We also controlled for age, gender, marital status,
education attainment, annual household income per capita, medical insurance, pension insurance, smoking,
drinking, GDP per capita, and proportion of the population over 65 years of age. Abbreviations: CRCHC,
coverage rate of the community health centres; CRCSI, coverage rate of the community service institutions.
4. Discussion
This study examined the relationship between RPs and PCCDs, with a focus on the
mediating roles of the coverage of CSIs and CHCs. Both policy content and mediation
analysis were conducted, demonstrating that RPs had positive impacts on PCCDs, while
both the coverage of CSIs and CHCs mediated the relationship between RPs and PCCDs.
Further, better SORM was associated with less NCD per capita, which showed that RPs
and PCCDs were more effective in regions with those qualities. This supports the rele-
Healthcare 2021,9, 1058 9 of 14
vant literature. For example, previous research has shown that the soundness of RPs can
effectively improve unhealthy lifestyles among patients with hypertension [
7
]. Similarly,
the soundness of RPs has also been found to promote maternal and child health [
25
]. In
this context, regions with better RPs have more effective PCCDs, particularly when RPs
are more comprehensive and cover a range of aspects, such as diet management and the
living environment. Meanwhile, the accountability mechanism is implementable when
relevant departments have clear responsibilities. For example, the government may raise
taxes on alcohol and tobacco while reducing the available subsidies for unhealthy foods.
Environmental protection standards should also be strictly implemented by environmental
protection departments. In other areas, the Landscaping bureau is responsible for the
construction of urban green spaces, while the Food and Drug Administration must su-
pervise the food processing industry to reduce the use of salt and trans-fatty acids [
37
].
Especially when combined, these efforts can further prevent and control chronic disease. It
is therefore necessary to establish a long-term working mechanism for managing general
health and chronic disease. Efforts must be directed at improving the contents and pro-
cesses of management services, clarifying departmental responsibilities, strengthening the
departmental accountability mechanism, and establishing a management model targeted
at the integrated prevention, treatment, and management of chronic disease [38].
Our study indicated that CHCs played a partial mediating role in the relationship
between RPs and PCCDs. As subsections of CSIs, CHCs were particularly effective entities
for controlling chronic disease [
39
]. Previous studies have shown that communities provide
better social environments, which facilitate access to elements such as health education,
early detection, early treatment, comprehensive management [
40
], and rehabilitation ex-
ercises [
41
], all of which can further aid in the control of chronic disease. CHCs play
important roles due to important aspects of social mobilisation and policy support [
42
], as
they work as the ‘gatekeeper’ of residents and ‘vanguard’ of prevention and control [
20
].
Community health personnel also maintain closer relationships with residents, thus pro-
viding the health management [
43
] needed to ensure positive health outcomes. In this
regard, China should establish and improve RPs specifically aimed at PCCDs, with a strong
focus on the incentive mechanism, thus allowing community health personnel to achieve a
balance between income and expenditures on capitation fees. In addition, the government
should allocate special chronic disease management funds within the per capita public
health funds in order to implement a pay-for-performance provision for community health
personnel [
44
]. This will enhance enthusiasm among community health personnel while
continuously strengthening the effects of PCCDs in various regions of China.
We found that CSIs played a major mediating role in the relationship between RPs
and PCCDs. The mediation analysis specifically demonstrated that the coverage of CSIs
had a more significant mediating effect than that derived from the coverage of CHCs.
This may be due to the widespread nature of social factors that affect chronic disease,
thus indicating that PCCDs should focus on cooperating with other fields at all levels. As
effective carriers for PCCDs, CSIs often undertake multiple responsibilities in addition to
those related to health services [
10
], such as providing social support at community service
centres [
44
], promoting physical exercise for the elderly in designated activity rooms [
45
],
employing community healthcare workers [
46
], providing medical care through community
pension service centres [
47
], and offering health self-education through reading rooms
or at community schools [
48
]. As a specific example, studies have shown that physical
exercise, social interaction, access to care, and community service have positive effects on
health of older patients with diabetes [
49
]. Along with the advantages provided through
community-based joint prevention and control, these factors play important roles in the
context of PCCDs. Based on the idea that health should be included in all policies, we
should consider that effective PCCDs may require coordinated participation from multiple
stakeholders [
50
]. In addition to improving CHCs, we should also pay more attention to
how multiple departments work as a joint force in the prevention and reduction of chronic
disease.
Healthcare 2021,9, 1058 10 of 14
The results of our policy content analysis showed that SORM requires improvement
in China. There are no relevant laws for PCCDs, owing to which the government is
unable to ensure adherence. There is also a great deal of room to develop completeness
among regulatory elements, including the policymaking process, service provisions, the
division of responsibility, and information monitoring and evaluation [
24
]. In addition,
the implementation of PCCDs is restricted by factors such as the lack of organisations
or professionals and effective multi-department coordination mechanisms [
6
]. Further,
the responsibilities of relevant departments are not sufficiently clear, thereby resulting
in the non-assessment of business objectives and the failure to play a positive role in
guiding relevant work. Departments also lack an external supervisory mechanism that
could effectively restrict their actions. In this context, the government should work to
improve laws and regulations related to the control of chronic disease, enhance the contents
of chronic disease management, clarify departmental responsibilities, and improve the
regulatory and accountability mechanisms. In sum, these efforts will help prevent and
reduce the occurrence of chronic disease.
This study also had some limitations. First, the policy content analysis was primarily
aimed at evaluating RPs. In the future, relevant verification information should be collected
through additional investigations. Second, mediating roles may not be limited to CSIs
and CHCs, as both the quality and type of service may affect the relationship between
RPs and PCCDs. Third, the factors affecting PCCDs are not limited to RPs; they also
include organisations, resources, and the environment. These factors are interrelated and
interact within the health system. Additional research is needed to verify the nature of
those relationships.
5. Conclusions
This study found that CSIs and CHCs play mediating roles in the relationship between
RPs and PCCDs. In this regard, RPs can promote the effects of PCCDs, meaning that
improved RPs will promote health while preventing and controlling disease. Moreover,
increased attention should be placed on the specific roles played by CSIs and CHCs.
Author Contributions:
Conceptualization, C.L. and M.H.; methodology, P.S., Q.S., Z.Z., Z.C., C.P.,
L.X., Z.H., A.M., Z.G., T.X., P.W., H.W. and C.H.; software, H.H., Q.Y. and Q.Z.; validation, H.H., Q.Y.
and L.L.; formal analysis, H.H., Q.Y. and C.L.; data curation, H.H., Q.Y., C.L. and M.H.; writing—
original draft preparation, H.H. and Q.Y.; writing—review and editing, Q.S., Z.Z., Z.C., C.P., L.X., C.L.
and M.H.; supervision, Z.H., A.M., Z.G., T.X., P.W., H.W., C.H., C.L. and M.H.; project administration,
C.L. and M.H.; funding acquisition, C.L. and M.H.; H.H. and Q.Y. contributed equally to this work.
All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Three-Year Action Plan of Shanghai Municipality Strength-
ens Public Health System Construction, grant number GWIV-32 and GWV-12, the National Natural
Science Foundation of China, grant number 72074048 and 71774031, and the Shanghai Foundation
for Talents Development, grant number 2020128. The funding bodies played no role in study design,
the collection, analysis, and interpretation of data, or in writing the manuscript for publication.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Some data used in this study are publicly available at http://charls.
pku.edu.cn/index/zh-cn.html (accessed on 13 September 2019) and http://www.stats.gov.cn/tjsj/
ndsj/2016/indexch.htm (accessed on 26 September 2019). The rest of the data are available from the
corresponding author on reasonable request.
Acknowledgments:
We are very grateful for the guidance of teachers and the support and help of
team members. We also thank Tsinghua University, Shandong University, Huazhong University of
Science and Technology, Anhui Medical University, Nanjing Medical University, Harbin Medical
University, Chongqing Medical University, Xinjiang Medical University, Weifang Medical University,
and Jining Medical University for their support of data collection.
Healthcare 2021,9, 1058 11 of 14
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
PCCDs prevention and control of chronic diseases
RPs regulatory policies
CSIs community service institutions
CHCs community health centres
SORM soundness of the regulatory mechanism
RECR regulatory element coverage rate
DPCR departmental responsibilities clarity rate
RMAR regulatory mechanism authority rate
AMCR accountability mechanism clarity rate
NCDs number of chronic diseases
ICC intraclass correlation coefficient
CRCSI coverage rate of the community service institutions
CRCHC coverage rate of the community health centres
SD standard deviation
IQR interquartile range
Appendix A
Table A1. Coding Template.
Name—ID Description Coding
Comprehensive
coverage of regulatory
elements—A1
Record the content form
involved in the file set
1—long-term goal (over five years)
2—short-term goal (under five years)
3—put forward tasks and measure around the goal
4—policymaking
5—service (intervention) content
6—service (intervention) scope (region, population)
7—service process
8—operational norms
9—technical standards
10—institutional settings standards
11—personnel allocation standards
12—professional qualification standards
13—funding sources
14—funding standards
15—funds guarantee measures
16—material price standards
17—material supply management norms
18—information system construction standards
19—information monitoring standards
20—division of responsibility
21—monitoring and control mode
22—performance indexes and standards
23—reward and punishment measures
24—department coordination modes
25—evaluation indicators and standards
Healthcare 2021,9, 1058 12 of 14
Table A1. Cont.
Name—ID Description Coding
Well-defined
responsibilities of stakeholders—A2
A2-1: record department names
1—provincial government
2—health commission
3—public health agencies
4—hospitals
5—primary health care institutions
6—finance bureau
7—human resources and social security bureau
8—policy security departments
9—health care security administration
10—education commission
11—civil affairs bureau
12—agriculture and rural affairs bureau
13—trading department
14—transportation commission
15—drug regulation administration
16—construction department
17—sports administration
18—environmental bureau
19—communication and news department
20—industrial information department
21—work safety administration
22—non-government organisations
A2-2: record the responsibility
description of each department
0—not mentioned
1—department responsibility mentioned in the policy documents
A2-3: record the measurable
responsibility description of each
department
0—not mentioned
1—measurable department responsibility mentioned in the policy
documents
Authority of regulatory
mechanism—B1
B1-1: record the government
level in charge of the coordination of
relevant departments
1—coordinate the government
2—coordinate the health commission
3—coordinate public health agencies
4—coordinate hospitals
5—coordinate primary health care institutions
6—coordinate the finance bureau
7—coordinate the human resources and social security bureau
8—coordinate policy security departments
9—coordinate the health care security administration
10—coordinate the education commission
11—coordinate the civil affairs bureau
12—coordinate the agriculture and rural affairs bureau
13—coordinate the trading department
14—coordinate the transportation commission
15—coordinate the drug regulation administration
16—coordinate the construction department
17—coordinate the sports administration
18—coordinate the environmental bureau
19—coordinate the communication and news department
20—coordinate the industrial information department
21—coordinate the work safety administration
22—coordinate non-government organisations
B1-2: record file publishing level
1—documents issued by legislative institution
2—documents issued by government
3—documents issued by multisectoral combination
4—documents issued by health commission
5—documents issued by public health agencies
Well-defined regulatory
accountability mechanism—B2
Record the accountability
mechanisms
0—not mentioned
1—regulator and accountability mentioned in the policy documents
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