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Article

The Efficiency of Primary Health Care Institutions in the Counties of Hunan Province, China: Data from 2009 to 2017

1
Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410078, China
2
Department of Primary Health Care, Health Commission of Hunan Province, Changsha 410078, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(5), 1781; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051781
Submission received: 24 December 2019 / Revised: 6 March 2020 / Accepted: 7 March 2020 / Published: 9 March 2020
(This article belongs to the Section Health Economics)

Abstract

:
This study aimed to estimate the efficiency and its influencing factors of Primary Health Care Institutions (PHCIs) in counties in Hunan Province, China, and put forward feasible suggestions for improving the efficiency of PHCIs in Hunan Province. We applied the Input-Oriented Data Envelopment Analysis (DEA) method and the Malmquist Index Model to estimate the efficiency of PHCIs in 86 counties in Hunan Province from 2009 to 2017. Then, the Tobit model was used to estimate the factors that influence the efficiency of PHCIs. Since the implementation of the new health-care reform in 2009, the number of health resources in PHCIs in Hunan Province has increased significantly, but most counties’ PHCIs remain inefficient. The efficiency of PHCIs is mainly affected by the total population, city level, the proportion of health technicians and the proportion of beds, but the changes in per capita GDP have not yet played a significant role in influencing efficiency. In the future, the efficiency of PHCIs should be improved by increasing medical technology skills and enthusiasm of health technicians and by improving the payment policies of medical insurance funds.

1. Introduction

Achieving universal health coverage (UHC) is one of the targets of the World Health Organization (WHO). Primary Health Care (PHC) can meet the majority of a person’s health needs over the course of their life and health systems with strong PHC are needed to achieve UHC [1]. In China, Primary Health Care Institutions (PHCIs), which are the providers of PHC, not only undertake general disease diagnosis and treatment but also undertake basic public health services [2]. The current hospital-centered health service system consumes a large number of health funds, but it cannot meet the ever-changing needs of people and does not have long-term sustainability [3]. Compared with hospitals, PHCIs are more suitable for the prevention and control of major diseases, reducing the incidence of diseases at the source and improving the health of residents. PHCIs in China consist of community health service centers, community health service stations, township health centers and village clinics [4]. Since the new health-care reform, initiated in 2009, China has introduced a series of policy measures in the areas of medical insurance policies, hierarchical medical systems and general practitioner systems to improve the quality of PHCIs’ services and guide residents to first visit PHCIs [5,6,7].
In the past nine years, the new health-care reform has made some progress, but there are also some problems [8]. According to the Statistical Communique on the Development of China’s Health Care Industry, the number of beds and the number of health technicians in PHCIs have increased by 38.98% and 36.66% from 2009 to 2017, respectively. However, their proportion of all medical institutions has decreased by 5.65% and 5.25%, respectively. Meanwhile, the number of outpatients/inpatients in PHCIs accounted for 61.75%/31.01% of the total outpatients/inpatients in 2009, yet decreased to 54.16%/18.21% in 2017 [9,10]. Affected by factors such as geography, the economy and policies, the provision and utilization of PHC in China varies from region to region, and between urban and rural areas [11,12,13]. With the construction of China’s county medical alliances, it is extremely important to improve the efficiency of PHCIs in the counties. Therefore, it is necessary to measure the efficiency of PHCIs, which could provide a policy basis for the health administration departments, and better guide the distribution and utilization of health resources.
Data Envelopment Analysis (DEA) is a non-parametric approach that uses linear programming to build a piece-wise linear-segmentation efficiency frontier based on best practice [14]. DEA has become an effective tool for measuring the efficiency of health care since the mid-1980s, and has been widely used in PHC over the past two decades [15,16]. Scholars usually evaluate PHC from two perspectives—institutions and regions—and some scholars evaluate the specific services, such as oral health care in PHC [17,18,19]. In China, studies from the perspectives of institutions and provinces have shown that the efficiency of PHCIs is low [20,21,22,23]. However, there are few studies that have evaluated the efficiency of PHCIs from the perspective of counties.
In this paper, we focused on the efficiency of PHCIs in 86 counties in Hunan Province, China. Hunan Province is located in central China and has 18 county-level cities, 61 counties, seven autonomous counties and 36 municipal districts. Its population was 68.89 million in 2018, of which the urban population accounts for 56.02% [24]. In recent years, Hunan Province has been committed to the construction of PHCIs to meet the health needs of the residents. While much has changed in Hunan’s PHCIs, little is known about the efficiency. This article studies the service efficiency and its influencing factors of PHCIs in Hunan Province from 2009 to 2017 in units of counties, and puts forward feasible suggestions for improving the efficiency of PHCIs in Hunan Province. The main objectives of this paper were: (1) To measure the efficiency of Hunan’s PHCIs during the new health-care reform from 2009 to 2017; (2) to evaluate changes in productivity during the new health-care reform; (3) to determine the influencing factors of Technical Efficiency (TE); and (4) to make feasible suggestions for improving the efficiency of PHCIs.

2. Materials and Methods

2.1. DEA

DEA is a nonparametric method for examining the relative efficiency of similar decision-making units (DMUs) with multiple inputs and outputs. It was first proposed by Charnes, Cooper and Rhodes in 1978 [25]. There are two important assumptions in the efficiency measurement of DEA [26]. One important decision to make when performing DEA is whether to use an input-orientation or output-orientation. The input-oriented model holds the current level of output constant and minimizes inputs, whereas an output-oriented model maximizes output while keeping the number of inputs constant. Another important theoretical hypothesis in DEA is whether the return to scale is constant or the returns to scale are variable. The first nonparametric models for efficiency estimation by Charnes, Cooper and Rhodes (CCR) assumed constant returns to scale (CRS) [25]. Later on, Banker, Charnes and Cooper (BCC) incorporated variable return to scale (VRS) to account for firms, which do not operate at their optimal scale [27]. At present, the choice of medical institutions in China depends mainly on patients and it is difficult to estimate the need for health services. Moreover, the government controls the input more than the output. Therefore, this paper chose to use the input-oriented BCC model to measure the annual TE from 2009 to 2017. The input-oriented BCC model is presented below:
m i n h = h ( X 0 , Y 0 )
s u b j e c t   t o                     h X 0 j = 1 n λ J ˙ X j 0 , j = 1 n λ j Y j ˙ Y 0 , j ˙ = 1 n λ j = 1 , λ J ˙ 0 , j = 1 , , n
This relies on the fact that h ≥ 0 will be satisfied when the components of every Xj and Yj are all nonnegative—as is the case for the observational data we are considering. This is a linear programming problem, the duality of which can be written as:
m a x r = 1 s u r y r 0 u 0
  s u b j e c t   t o                           r = 1 s u r y r j i = 1 m ν i x i j u 0 0  
i = 1 m ν i x i 0 = 1 , u r , ν i 0
where u 0 is unconstrained in sign. If the h ( X 0 , Y 0 ) value is = 1, it is efficient; h ( X 0 , Y 0 ) value < 1, it is inefficient.

2.2. Malmquist Index Model

The Malmquist index model is a non-parametric DEA model based on the productivity index research method first proposed by Malmquist. In 1982, Caves et al. first introduced the Malmquist Index (MI) as an efficiency index into the field of production analysis [28]. The Malmquist index model measures the change in total factor productivity change (TFP) of DMUs over time. The TFP is divided into two parts: Technical change (TECHCH) and technical efficiency change (EFFCH). The technical efficiency can be further decomposed into the product of pure technical efficiency change (PECH) and scale efficiency change (SECH). The input-oriented MI consists of four input-oriented distance functions that functionally represent multiple-input and multiple-output production techniques. They can be used to characterize efficiency because the distance function is the reciprocal of the technical efficiency measure proposed by Farrell [29]. The distance function is a natural way of modeling production boundaries because the deviations and offset from this boundary represent changes in efficiency and technology, respectively. This paper uses the Malmquist index model to measure the efficiency changes in Hunan Province from 2009 to 2017. The change in productivity between period t (base period) and period t + 1 is defined as:
M 0 ( x t + 1 , y t + 1 , x t , y t ) = D 0 t ( x t + 1 , y t + 1 ) D 0 t ( x t , y t ) × D 0 t + 1 ( x t + 1 , y t + 1 ) D 0 t + 1 ( x t , y t ) = D 0 t ( x t + 1 , y t + 1 ) D 0 t ( x t , y t ) D 0 t ( x t + 1 , y t + 1 ) D 0 t + 1 ( x t + 1 , y t + 1 ) × D 0 t ( x t , y t ) D 0 t + 1 ( x t , y t )
where M0 is the input-oriented Malmquist productivity index, and y represents the output vector that can be produced using input vector x . Among them, EFFCH = D 0 t ( x t + 1 , y t + 1 ) D 0 t ( x t , y t ) , TECHCH = D 0 t ( x t + 1 , y t + 1 ) D 0 t + 1 ( x t + 1 , y t + 1 ) × D 0 t ( x t , y t ) D 0 t + 1 ( x t , y t ) , respectively, indicate that efficiency improvement and technological progress occurred in the period t to t+1. Therefore, TFP can be expressed as follows: TFP = EFFCH × TECHCH. If the TFP > 1, indicates that the TFP of the t+1 period is higher than that of the t period; a TFP < 1, indicates a decrease in TFP; and TFP = 1 indicates that TFP is unchanged.

2.3. Tobit

In this study, the TE obtained by DEA is the dependent variable, and the main factors affecting the TE of PHCIs in the county are explored by constructing regression models. Since the efficiency value measured by DEA ranges from [0, 1]; that is, the value less than 0 and greater than 1 is punctured, the dependent variable with punctured feature belongs to the restricted dependent variable. Estimating the regression parameters using a regression model based on the ordinary least squares method is likely to cause an estimation bias due to incomplete data. In view of this, Tobin first proposed the puncturing regression model, or the Tobit model, in 1958 [30]. The formula for the Tobit regression equation is the following:
Y i = β 0 + i = 1 n β i X i + μ , i = 1 , 2 , , n
where Y i is the TE value, X i is the explanatory variable, and β i is the coefficient to be evaluated.

2.4. Data and Selection of Variables

Considering the integrity and availability of data, this study included 86 counties (county-level cities) in Hunan Province from 2009 to 2017; one county was not included because of partial missing data. The data were collected from the National Health Statistical Information Report System during the period 2009–2017, and the Hunan Statistics Yearbook.
In this paper, the input and output variables were selected by reviewing the relevant literature and based on the availability of data in the National Health Statistical Information Report System [16,31,32,33]. Regarding the input variables, both human resources and facilities were considered important for health care services. We selected the number of health technicians instead of human resources, and took the number of beds and the amount of equipment valued at more than 10,000 RMB to replace the facilities. The output indicators selected in this paper are the number of outpatients and emergency visits, and the number of discharged patients (refer to the standard explanation, Table 1).
In the Tobit regression analysis, influencing factors that affect PHCI’s efficiency indirectly were the total population, city level, per capita GDP, the proportion of health technicians, the proportion of beds (refer to the standard explanation, Table 1).
The input and output variables were descriptively analyzed using SPSS 22.0 (International Business Machine, New York, America). The software DEAP 2.1 (University of New England, Armidale, Australia) was used to calculate the value of Technical Efficiency (TE) and Total Factor Productivity (TFP) and used the software STATA 12.0 (StataCorp LLC, Texas, TX, USA) for Tobit regression analysis.

3. Results

3.1. Descriptive Statistics of Inputs and Outputs

Table 2 shows the statistics of the input and output variables. From 2009 to 2017, the means of discharged patients, health technicians, beds and equipment valued at more than 10,000 RMB increased annually. During the same time, the number of outpatients and emergency visits has increased year by year from 2009 to 2013, and has decreased year by year from 2014 to 2015; after rising in 2016, it fell again.
As can be seen from the percentages of PHCI’s input and output indicators in all medical institutions, the outpatients and emergency visits, the discharged patients, health technicians, beds and equipment all showed a downward trend from 2009 to 2017. In addition to beds and discharged patients, other indicators increased significantly from 2009–2011, followed by a significant decline from 2012 to 2014, and a slowdown in 2015–2017 (Figure 1).

3.2. Results of the DEA Model

As shown in Table 3, for the years 2009–2017, the TE of PHCIs in 86 counties in Hunan Province increased from 0.559 in 2009 to 0.754 in 2014, and increased slightly in 2017 after a small decline in 2016. Among the counties with effective TE, there were at least 5 (5.81%) counties in 2009, and the most in 2015 when there were 14 (16.28%).
In 2009–2017, there were about 5 (5.81%)–17 (19.77%) counties whose SE operated at the best level, with an SE score of 1.000. The average SE’s range was 0.813–0.928.
During the period from 2009 to 2015, TEvrs increased from 0.697 to 0.819. After a decrease in 2016, it increased again in 2017. The number of effective counties fluctuated between 16 and 24.

3.3. Results of the Malmquist Index Model

The Malmquist index model was applied to analyses on the changes in productivity over the 2009–2017 period, and the year 2009 was taken as the technical reference. Table 4 presents the Malmquist index summary of annual geometric means. On average, TFP decreased by 3.1%, among which, TECHCH decreased by 6.5% and EFFCH increased slightly by 3.7%. During the period 2009–2017, 24 (27.91%) counties had TFP score greater than 1, indicating growth in TFP; 61 (70.93%) counties had TFP score less than 1, indicating a deterioration in TFP. Figure 2 shows the distribution of TFP in Hunan Province. The most efficient counties are Xiangyin County (30) and Lengshuijiang City (77). The TFP of each county is shown in Table A1, Appendix A.

3.4. Results of Tobit Regression

In this article, Tobit regression was used to analyze the influencing factors of TE during 2009–2017. The results are presented in Table 5. In order to reduce heteroscedasticity, we have performed a logarithmic conversion on the two independent variables of population and per capita GDP. Regarding environmental factors, Ln (total population) (p < 0.001), city level (p < 0.001), the proportion of health technicians (p < 0.001), the proportion of beds (p = 0.002) exhibited significance, indicating that they are significant in influencing the efficiency of PHCIs. However, Ln (per capita GDP) (p = 0.977) was statistically insignificant.

4. Discussion

Since the new health-care reform in 2009, the government has invested a lot in monetary, equipment, buildings and health technicians in PHCIs, but the growth of these resources has not brought about an increase in the use of PHCIs, residents’ choice of health care services still tends to tertiary hospitals [8,35]. In May 2019, the National Health Commission of People’s Republic of China issued a policy document requiring the overall performance of the county-level health system and the ability of PHCIs to be improved, and the visit rate of PHCIs in the county is required to reach 65% [36]. However, in recent years, the visit rate of PHCIs has been declining. This paper can provide a reference for improving the efficiency of PHCIs by studying the efficiency of PHCIs in 86 counties of Hunan Province and their influencing factors.
The BCC model is a method used to analyze the efficiency of a single period. This study used the BCC model to measure the annual efficiency of PHCIs in Hunan Province each year from 2009 to 2017. It was found that the average TE of PHCIs in Hunan Province in the past nine years was lower than 0.8 and most counties were ineffective, which indicates that the efficiency of PHCIs needs to be improved. Besides, the TE and the counties with effective TE both increased from 2009 to 2015 but declined in 2016. While we cannot know the trend of efficiency after 2018, a potential explanation is that the dissolution and consolidation of PHCIs in Hunan Province during 2015–2016 that affected the continuity of PHC and according to the general choice of patients and increasingly convenient traffic conditions, patients who previously went to the revoked PHCIs may not go to the retained PHCIs, but are more likely to a secondary or tertiary hospital [37,38,39]. Compared to SE, TEvrs was lower, which indicates that while improving the scale of PHCIs, we must pay more attention to improving efficiency by improving the medical technology of PHCIs. Compared with other studies using the same method, the average TE of PHCIs in Hunan Province was higher than that in the Community Health Centers of Jiangsu, which is 0.15–0.40 [40], but lower than China’s total medical services efficiency, which is 0.904 [41]. This difference may be due to different regions or research subjects.
The Malmquist Index Model measures the dynamic efficiency change of the DMU over multiple periods. The results of the Malmquist Index Model verified that PHCIs in Hunan experienced a significant decline in TFP from 2009 to 2017, which was consistent with the results of Zhang et al. [23]. The trend of TECHCH and TFP was consistent, so it can be considered that the change of TFP was mainly affected by TECHCH changes, which is consistent with the findings of Leng et al. [4]. Chen et al. pointed out that technological progress determines the key factors of the TFP of community health services in China [42]. The main reason for the low TECHCH is the lack of excellent health technicians, and the underutilization of medical equipment in PHCIs.
Some studies indicate that health resources in areas with low economic levels can be better utilized, which may increase efficiency [18,43,44]. In this study, the Tobit regression results indicated that the impact of the per capita GDP on the growth of TE of PHCIs is not significant. This is consistent with the findings of Leng et al. who found that this is mainly due to the guiding role of government policies and the implementation of health-care reform policies that have narrowed the gap in regional health resource allocation [4]. Therefore, the level of economic between regions has a small impact on TE.
The positive impact of the total population on TE in this study is consistent with the findings of Tan et al. [45]. The number of permanent residents at the end of the year reflects the potential health care service needs in the region, which will affect the efficiency of the hospital. In addition, the increase in China’s population is accompanied by the implementation of the two-child policy and the development of an aging population, which has also increased the demand and utilization of health services [46,47]. However, research by Chen et al. suggested that a large population will increase the burden on the hospital and make health care service inefficient, and this may be caused by different research objects [48].
This study shows that the city level has a negative impact on TE. At the same time, Zhang et al. showed that the impact of urbanization on the efficiency of PHCIs in the rural and urban areas is different [49]. With the advancement of urbanization in China, the number of poor people has decreased. Zhang et al. showed that wealthy people are more likely to use hospitals with adequate resources, and poorer people are more likely to use poorly funded PHCIs for health care services [50]. Meanwhile, due to the relatively concentrated population of urbanization and the convenience of modern transportation, which has further improved the geographic accessibility of health care services, residents may be more inclined to seek higher quality health care services at secondary or tertiary hospitals, which has a negative impact on the efficiency of PHCIs [51].
While the new health-care reform has increased the number of health technicians and beds in PHCIs, TE has decreased as their proportion increased. In the long run, a lack of qualified health technicians and inadequate use of medical equipment may reduce technical efficiency [52]. PHCIs still have problems such as severe maldistribution of quality human resources, low education of health technicians and the detrimental elements of health-care reform policies [53]. These problems have led to a lack of patients’ trust in the quality of PHC. Therefore, patients’ demands for high-quality health care services make the gradient design of the health insurance reimbursement rate insufficient to guide patients to PHCIs for treatment [20,54]. Meanwhile, due to the small development space, low wages and lack of sufficient performance incentives of PHCIs, the enthusiasm of health technicians has been affected, limiting the improvement of PHCIs’ efficiency [5,55,56]. After the implementation of the new health-care reform, the government provided a lot of advanced medical equipment for PHCIs, but the health technicians in the PHCIs lacked operating skills and could not use the equipment well [57,58]. As a result, a large amount of equipment and beds were idle, which hindered PHCIs in improving efficiency.
This study conducted a horizontal and vertical analysis of the efficiency of PHCIs from 2009 to 2017 from the perspective of the county, and explored its influencing factors. However, there are some limitations. First, the input-output variables and influencing factors in this paper were selected based on the literature and the availability of data, which may lead to deviations in the research results. Second, the TE of PHCIs may be affected by other factors not considered in this article, such as government health expenditure, the education level of the residents, life expectancy, et al. Third, bias adjustments of TE and TFP scores were not carried out due to the limitation of the basic DEA approach. Fourth, due to the availability of data, we only use the data from 2009 to 2017 for analysis, which cannot be compared with the efficiency of PHCIs in other periods. Despite these limitations, this study can be considered a useful attempt to measure the TE and TFP of PHCIs at the county level in China.

5. Conclusions

In this study, we utilized a panel dataset of 86 county-level units of Hunan during the period 2009–2017 to describe the allocation of health resources and the provision of health care services in PHCIs, to compare the efficiency of the regional primary health care systems, and to explore the determinants of TE. The main conclusions of this study were as follows: Firstly, the efficiency of PHCIs in Hunan Province during 2009–2017 was low, and most counties are inefficient. Secondly, the results of the Malmquist index showed that the PHCIs in Hunan Province experienced significant decline in TFP from 2009 to 2017, which mainly resulted from the low TECHCH. Third, the factors affecting the TE of PHCIs are the total population, city level, the proportion of health technicians and the proportion of beds in PHCIs.
According to the conclusions, we propose the following suggestions: First, we need to improve the medical technology skills of health technicians, strengthening the training of clinical skills, health management skills and equipment operation skills of health technicians, and creating policies on job title and continuing education could help to attract excellent health technicians to work in PHCIs. Second, improving the performance policy of PHCIs would allow PHCIs to independently determine internal performance wages and distribution methods, and increase the proportion of incentive performance wages on the premise of guaranteeing the payment of basic wages, thereby enhancing the enthusiasm of health technicians. Third, improving the payment policy of medical insurance funds, by increasing the total annual budgetary payment of PHCIs in the county medical insurance fund. The increase should be higher than the evaluation increase of the total annual budget payment of local medical institutions, and should shape the medical insurance payment policy to guide residents who have common diseases or frequently occurring diseases to go to PHCIs for health care service.

Author Contributions

Conceptualization, L.C. and K.Z.; methodology, K.Z.; validation, S.C., K.Z. and H.C.; formal analysis, K.Z., S.C. and F.L.; resources, H.C. and F.L.; data curation, F.L.; writing—original draft preparation, K.Z.; writing—review and editing, L.C. and S.C.; supervision, L.C.; project administration, L.C.; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Central South University Graduate Survey Project in 2018, grant number 2018dcyj066.

Acknowledgments

Thanks to Miss Xinyin Wu for suggestions on this article. Thanks to the Health Commission of Hunan Province for providing data.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Abbreviations

UHCUniversal Health Coverage
WHOWorld Health Organization
PHCPrimary Health Care
PHCIsPrimary Health Care Institutions
DEAData Envelopment Analysis
TETechnical Efficiency
DMUsdecision-making units
CCRCharnes, Cooper, and Rhodes
BCCBanker, Charnes, and Cooper
CRSconstant returns to scale
VRSvariable return to scale
MIthe Malmquist Index
TFPtotal factor productivity change
TECHCHtechnical change
EFFCHtechnical efficiency change
PECHpure technical efficiency change
SECHscale efficiency change

Appendix A

Table A1. TFP in 86 counties (county-level cities) in Hunan Province from 2009 to 2017.
Table A1. TFP in 86 counties (county-level cities) in Hunan Province from 2009 to 2017.
NumberCountiesEfficiency
1 Changsha County1.049
2 Ningxiang County1.019
3 Liuyang City1.019
4 Zhuzhou County1.012
5 You County0.985
6 Chaling County1.012
7 Yanling County0.985
8 Liling County1.007
9 Xiangtan County0.936
10 Xiangxiang City0.933
11 Shaoshan City0.922
12 Hengyang County0.961
13 Hengnan County1.001
14 Hengshan County0.913
15 Hengdong County0.880
16 Qidong County0.939
17 Leiyang City0.956
18 Changning City1.030
19 Shaodong County0.964
20 Xinshao County1.001
21 Shaoyang County1.014
22 Longhui County0.969
23 Dongkou County0.964
24 Suining County1.001
25 Xinning County0.959
26 Chengbu County1.018
27 Wugang City0.947
28 Yueyang County0.929
29 Huarong County0.856
30 Xiangyin County1.112
31 Pingjiang County0.999
32 Miluo City0.916
33Linxiang City0.821
34 Anxiang County1.006
35 Hanshou County0.973
36 Li County0.981
37 Linli County1.019
38 Taoyuan County1.016
39 Shimen County1.016
40 Jinshi City0.983
41 Cili County1.028
42 Sanzhi County0.950
43 Nan County0.967
44 Taojiang County0.972
45 Anhua County0.963
46 Yuanjiang City0.986
47 Guiyang County0.939
48 Yizhang County1.016
49 Yongxing County0.969
50 Jiahe County1.032
51 Linwu County0.989
52 Rucheng County0.976
53 Guidong County0.917
54 Anren County0.918
55 Zixing County0.955
56 Qiyang County0.980
57 Dongan County0.888
58 Shuangpai County0.954
59 Dao County0.877
60 Jiangyong County0.889
61 Ningyuan County0.919
62 Lanshan County0.885
63 Xintian County0.918
64 Jianghua County0.954
65 Zhongfang County0.985
66 Yuanling County0.979
67 Chenxi County1.036
68 Xupu County0.999
69 Huitong County0.978
70 Mayang County0.890
71 Zhijiang County0.966
72 Jingzhou County0.946
73 Tongdao County1.045
74 Hongjiang City0.929
75 Shuangfeng County0.942
76 Xinhua County0.973
77 Lengshuijiang City1.143
78 Lianyuan City1.000
79 Jishou City0.907
80 Luxi County1.051
81 Fenghuang County0.982
82 Huayuan County0.996
83 Baojing County0.906
84 Guzhang County0.954
85 Yongshun County0.997
86 Longshan County0.994

References

  1. Ten Threats to Global Health in 2019. Available online: https://www.who.int/news-room/feature-stories/ten-threats-to-global-health-in-2019 (accessed on 1 May 2019).
  2. Deng, F.; Lv, J.H.; Wang, H.L.; Gao, J.M.; Zhou, Z.L. Expanding public health in China: An empirical analysis of healthcare inputs and outputs. Public Health 2017, 142, 73–84. [Google Scholar] [CrossRef] [PubMed]
  3. Yang, G.H.; Wang, Y.; Zeng, Y.X.; Gao, G.F.; Liang, X.F.; Zhou, M.G.; Wan, X.; Yu, S.C.; Jiang, Y.H.; Naghavi, M. Rapid health transition in China, 1990–2010: Findings from the Global Burden of Disease Study 2010. Lancet Oncol. 2013, 381, 1987–2015. [Google Scholar] [CrossRef]
  4. Leng, Y.; Liu, W.W.; Xiao, N.N.; Li, Y.N.; Deng, J. The impact of policy on the intangible service efficiency of the primary health care institution- based on China’s health care reform policy in 2009. Int. J. Equity. Health 2019, 18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Ma, X.C.; Wang, H.; Yang, L.; Shi, L.Y.; Liu, X.Y. Realigning the incentive system for China’s primary healthcare providers. BMJ 2019, 365. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. General Office of the State Council Guidance of the General Office of the State Council on Promoting Tiered Delivery System. Available online: http://www.gov.cn/zhengce/content/2015-09/11/content_10158.htm (accessed on 15 August 2019).
  7. General Office of the State Council Opinions on Reforming and Improving the Incentives to Train and Employ General Practitioners. Available online: http://www.gov.cn/zhengce/content/2018-01/24/content_5260073.htm (accessed on 18 August 2019).
  8. Li, X.; Lu, J.; Hu, S.; Cheng, K.K.; De Maeseneer, J.; Meng, Q.; Mossialos, E.; Xu, D.R.; Yip, W.; Zhang, H.; et al. The primary health-care system in China. Lancet Oncol. 2017, 390, 2584–2594. [Google Scholar] [CrossRef]
  9. Statistical Communique on the Development of China’s Health Care Industry in 2017. Available online: http://www.nhc.gov.cn/guihuaxxs/s10748/201905/9b8d52727cf346049de8acce25ffcbd0.shtml (accessed on 16 August 2019).
  10. Statistical Communique on the Development of Health Care in China in 2009. Available online: http://www.nhc.gov.cn/mohwsbwstjxxzx/s7967/201004/46556.shtml (accessed on 16 August 2019).
  11. Zhang, X.Y.; Zhao, L.; Cui, Z.; Wang, Y.G. Study on equity and efficiency of health resources and services based on key indicators in China. PLoS ONE 2015, 10. [Google Scholar] [CrossRef] [Green Version]
  12. Wang, X.L.; Yang, H.Z.; Duan, Z.Q.; Pan, J. Spatial accessibility of primary health care in China: A case study in Sichuan Province. Soc. Sci. Med. 2018, 209, 14–24. [Google Scholar] [CrossRef]
  13. Dong, X.X.; Liu, L.; Cao, S.Y.; Yang, H.J.; Song, F.J.; Yang, C.; Gong, Y.H.; Wang, Y.X.; Yin, X.Y.; Xu, X.; et al. Focus on vulnerable populations and promoting equity in health service utilization. Public Health 2014, 14. [Google Scholar] [CrossRef] [Green Version]
  14. Hollingsworth, B.; Dawson, P.J.; Maniadakis, N. Efficiency measurement of health care: A review of non-parametric methods and applications. Health Care Manag. Sci. 1999, 2, 161–172. [Google Scholar] [CrossRef]
  15. Sherman, H.D. Hospital efficiency measurement and evaluation. Empirical test of a new technique. Med. Care 1984, 22, 922–938. [Google Scholar] [CrossRef]
  16. Pelone, F.; Kringos, D.S.; Romaniello, A.; Archibugi, M.; Salsiri, C.; Ricciardi, W. Primary care efficiency measurement using data envelopment analysis: A systematic review. J. Med. Syst. 2015, 39. [Google Scholar] [CrossRef] [PubMed]
  17. Cordero Ferrera, J.M.; Crespo Cebada, E.; Murillo Zamorano, L.R. The effect of quality and socio-demographic variables on efficiency measures in primary health care. Eur. J. Health Econ. 2014, 15, 289–302. [Google Scholar] [CrossRef] [PubMed]
  18. Ahmed, S.; Hasan, M.Z.; MacLennan, M.; Dorin, F.; Ahmed, M.W.; Hasan, M.M.; Hasan, S.M.; Islam, M.T.; Khan, J.A.M. Measuring the efficiency of health systems in Asia: A data envelopment analysis. BMJ Open 2019, 9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Linna, M.; Nordblad, A.; Koivu, M. Technical and cost efficiency of oral health care provision in finnish health centres. Soc. Sci. Med. 2003, 56, 343–353. [Google Scholar] [CrossRef]
  20. Zheng, D.; Gong, J.; Zhang, C. Efficiency of medical service systems in the rural areas of Mainland China: A comparative study from 2013 to 2017. Public Health 2019, 171, 139–147. [Google Scholar] [CrossRef]
  21. Liu, Q.; Li, B.; Mohiuddin, M. Prediction and Decomposition of Efficiency Differences in Chinese Provincial Community Health Services. Int J. Environ. Res. Public Health 2018, 15, 2265. [Google Scholar] [CrossRef] [Green Version]
  22. Cheng, Z.H.; Cai, M.; Tao, H.B.; He, Z.F.; Lin, X.; Lin, H.F.; Zuo, Y.L. Efficiency and productivity measurement of rural township hospitals in China: A bootstrapping data envelopment analysis. BMJ Open 2016, 6. [Google Scholar] [CrossRef] [Green Version]
  23. Zhang, Y.; Wang, Q.; Jiang, T.; Wang, J. Equity and efficiency of primary health care resource allocation in mainland China. Int J. Equity Health 2018, 17. [Google Scholar] [CrossRef] [Green Version]
  24. Introduction of Hunan Province. Available online: http://www.hunan.gov.cn/jxxx/hngk/sqjs/sqjs.html (accessed on 15 August 2019).
  25. Charnes, A.; Cooper, W.W.; Rhodes, E. Measuring the efficiency of decision making units. Eur. J. Ope. Res. 1978, 2, 429–444. [Google Scholar] [CrossRef]
  26. Varabyova, Y.; Schreyogg, J. International comparisons of the technical efficiency of the hospital sector: Panel data analysis of OECD countries using parametric and non-parametric approaches. Health Policy 2013, 112, 70–79. [Google Scholar] [CrossRef] [Green Version]
  27. Banker, R.D.; Charnes, A.; Cooper, W.W. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 1984, 30, 1078–1092. [Google Scholar] [CrossRef] [Green Version]
  28. Caves, D.W.; Christensen, L.R.; Diewert, W.E. The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica 1982, 50, 1393–1414. [Google Scholar] [CrossRef]
  29. Farrell, M.J. The Measurement of productive efficiency. J. R. Stat. Soc. Ser. A. 1957, 120, 253–290. [Google Scholar] [CrossRef]
  30. Tobin, J. Estimation of relationships for limited dependent variables. Econo. Soc. 1958, 26, 24–36. [Google Scholar] [CrossRef] [Green Version]
  31. Hadji, B.; Meyer, R.; Melikeche, S.; Escalon, S.; Degoulet, P. Assessing the relationships between hospital resources and activities: A systematic review. J. Med. Syst. 2014, 38. [Google Scholar] [CrossRef] [PubMed]
  32. Cantor, V.J.M.; Poh, K.L. Integrated analysis of healthcare efficiency: A systematic review. J. Med. Syst. 2018, 42, 23. [Google Scholar] [CrossRef]
  33. Kohl, S.; Schoenfelder, J.; Fugener, A.; Brunner, J.O. The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Manag. Sc. 2019, 22, 245–286. [Google Scholar] [CrossRef]
  34. National Health Commission of the People’s Republic of China. China Health Statistical Yearbook 2018; Peking Union Medical College Press: Beijing, China, 2018; pp. 24–73. [Google Scholar]
  35. Yip, W.; Fu, H.Q.; Chen, A.T.; Zhai, T.M.; Jian, W.Y.; Xu, R.; Pan, J.; Hu, M.; Zhou, Z.L.; Chen, Q.; et al. 10 years of health-care reform in China: Progress and gaps in Universal Health Coverage. Lancet Oncol. 2019, 394, 1192–1204. [Google Scholar] [CrossRef]
  36. Notice on Promoting the Construction of a Compact County Medical Alliances. Available online: http://www.nhc.gov.cn/jws/s3580/201905/833cd709c8d346d79dcd774fe81f9d83.shtml (accessed on 19 December 2019).
  37. Opinions of Hunan Provincial People’s Government of the Communist Party of China on the Reform of Township Divisions. Available online: http://www.hunan.gov.cn/szf/hnzb/2015/2015nd20q/swszfwj_99041/201510/t20151028_4701565.html (accessed on 7 January 2020).
  38. Li, W.Q.; Liu, J.; Tan, X.C. Study on the Influence of Township Cancellation and Mergence Policy on Township Hospitals. Sci. Edu. Art. Cul. 2019, 9, 189–192. [Google Scholar]
  39. Shen, W.R. On the revocation and combination of township hospitals. Chin. Rural Health Serv. Adm. 2001, 21, 24–25. [Google Scholar]
  40. Zhou, L.L.; Xu, X.L.; Antwi, H.A.; Na, W.L. Towards an equitable healthcare in China: Evaluating the productive efficiency of community health centers in Jiangsu Province. Int. J. Equity Health 2017, 16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Sun, J.; Luo, H. Evaluation on equality and efficiency of health resources allocation and health services utilization in China. Int. J. Equity Health 2017, 16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Chen, Z.R.; Song, B.X.; Gao, S. Studying on the changes of efficiency of community health services and differences decomposition under the background of new healthcare reform in China. Chin. Health Serv. Manag. 2017, 34, 94–98. [Google Scholar]
  43. Zhang, L.Y.; Cheng, G.; Song, S.H.; Yuan, B.B.; Zhu, W.M.; He, L.; Ma, X.C.; Meng, Q.Y. Efficiency performance of China’s health care delivery system. Int. J. Health Plann. Manag. 2017, 32, 254–263. [Google Scholar] [CrossRef]
  44. Balabanova, D.; Mills, A.; Conteh, L.; Akkazieva, B.; Banteyerga, H.; Dash, U.; Gilson, L.; Harmer, A.; Ibraimova, A. Good Health at Low Cost 25 years on: Lessons for the future of health systems strengthening. Lancet Oncol. 2013, 381, 2118–2133. [Google Scholar] [CrossRef]
  45. Tan, H.W.; Zheng, W.H.; Zhang, Y.; Yan, W.H.; Zhu, X.L.; Liu, X.; Zhang, P.L. Analysis of the cost effectiveness in county-level public hospitals in Chongqing and relevant influencing factors. J. Shanghai Jiaotong U. 2016, 36, 730–736. [Google Scholar]
  46. Zeng, Y.; Hesketh, T. The effects of China’s universal two-child policy. Lancet Oncol. 2016, 388, 1930–1938. [Google Scholar] [CrossRef] [Green Version]
  47. Wang, X.Q.; Chen, P.J. Population ageing challenges health care in China. Lancet Oncol. 2014, 383, 870. [Google Scholar] [CrossRef]
  48. Chen, F.; Xiang, Y.W.; Jiang, J.H.; Zhang, J.H. Analyzing The efficiency of operation of guangdong provincial traditional Chinese medicine hospital and its influencing factors. Chin. Health Serv. Manag. 2018, 35, 744–747. [Google Scholar]
  49. Zhang, H.B.; Shen, J.L.; Wang, C.Z. Empirical research on the services efficiency of Chinese basic medical and health institutions. Chin. Health Serv. Manag. 2017, 34, 76–80. [Google Scholar]
  50. Zhang, T.; Xu, Y.J.; Ren, J.P.; Sun, L.Q.; Liu, C.J. Inequality in the distribution of health resources and health services in China: Hospitals versus primary care institutions. Int. J. Equity Health 2017, 16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Hunan Statistics Yearbook in 2018. Available online: http://222.240.193.190/18tjnj/indexch.htm (accessed on 22 December 2019).
  52. Cheng, Z.H.; Tao, H.B.; Cai, M.; Lin, H.F.; Lin, X.J.; Shu, Q.; Zhang, R.N. Technical efficiency and productivity of Chinese county hospitals: An exploratory study in Henan province, China. BMJ Open 2015, 5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Wu, D.; Lam, T.P. Underuse of Primary Care in China: The Scale, Causes, and Solutions. J. Am. Board Fam. Med. 2016, 29, 240–247. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Jin, Y.Z.; Yuan, B.B.; Zhu, W.M.; Zhang, Y.G.; Xu, L.; Meng, Q. The interaction effect of health insurance reimbursement and health workforce on health care-seeking behaviour in China. Int. J. Health Plann. Manag. 2019, 34, 900–911. [Google Scholar] [CrossRef]
  55. Li, H.; Kong, P.; Yu, H.N.; Zhao, S.C.; Yuan, J.; Meng, Q.Y. Incentive factors influencing work behavior of primary care providers in China. Chin. J. Health Policy 2012, 5, 6–11. [Google Scholar]
  56. Li, H.W.; Yuan, B.B.; Meng, Q.Y.; Kawachi, I. Contextual Factors Associated with Burnout among Chinese Primary Care Providers: A Multilevel Analysis. Int. J. Environ. Res. Public Health 2019, 16, 3555. [Google Scholar] [CrossRef] [Green Version]
  57. Wang, C.; Zhang, L.; Yu, X.S.; Gao, Y.; Zhao, L.; Liu, Y. A study on the present situation of the service ability of the primary health care facilities in Chengdu. Health Econ. Res. 2018, 4, 48–51. [Google Scholar]
  58. He, J.L.; Tao, H.B.; Yu, W.H.; Jin, L. Study on allocation and utilization of medical equipment in primary medical institutions. Chin. Health Res. 2017, 20, 418–421. [Google Scholar]
Figure 1. The percentage of inputs and outputs of primary health care institutions in 86 counties in Hunan Province from 2009 to 2017. Note: Percentage = sum of input (output) of primary health care institutions in 86 counties/sum of input (output) of medical institutions in 86 counties
Figure 1. The percentage of inputs and outputs of primary health care institutions in 86 counties in Hunan Province from 2009 to 2017. Note: Percentage = sum of input (output) of primary health care institutions in 86 counties/sum of input (output) of medical institutions in 86 counties
Ijerph 17 01781 g001
Figure 2. Distribution of factor productivity change (TFP) in 86 counties (county-level cities) from 2009 to 2017.
Figure 2. Distribution of factor productivity change (TFP) in 86 counties (county-level cities) from 2009 to 2017.
Ijerph 17 01781 g002
Table 1. Definitions of input, output and influencing factors [34].
Table 1. Definitions of input, output and influencing factors [34].
CategoryVariableDefinitionUnit
InputNumber of health techniciansHealth technicians include licensed physicians, licensed assistant physicians, registered nurses, pharmacists, laboratory technicians, radiologists, health supervisors and interns. person
Number of bedsThe number of beds refers to the number of fixed beds at the end of the year, including regular beds, simple beds, monitoring beds, extra beds for more than half a year, beds being disinfected and repaired and beds disabled due to expansion or overhaul. quantity
The amount of equipment valued at more than 10,000 RMBThe amount of equipment of more than 10,000 RMBset
OutputNumber of outpatients and emergency visitsThe number of patients attending outpatient and emergency diagnostic servicesperson
Number of discharged patientsThe number of people discharged after hospitalization at the end of each yearperson
Influencing factorThe total populationThe population of this study refers to the population of each county at 24 o’clock on December 31 each year.10,000 persons
City levelThe proportion of the urban population in each county to the total population%
Per capita GDPPer capita GDP of each countyRMB
The proportion of health technicians in PHCIsThe proportion of health technicians in PHCIs to the number of health technicians in all medical institutions in the county%
The proportion of beds in PHCIsThe proportion of beds in PHCIs to the number of beds in all medical institutions in the county%
Table 2. Descriptive statistics of inputs and outputs.
Table 2. Descriptive statistics of inputs and outputs.
YearMeasureThe Number of Outpatients and Emergency VisitsThe Number of Discharged PatientsThe Number of Health TechniciansThe Number of BedsThe Amount of Equipment Valued at More than 10,000 RMB
2009Mean962,707.21 29,345.60 708.23 676.64 125.92
SD724,110.69 18,350.91 425.51 564.43 107.41
Min110,131.00 4518.00 116.00 150.00 11.00
Max3,953,181.00 114,585.00 1646.00 4577.00 811.00
2010Mean1,066,902.41 28,688.70 758.60 693.65 154.16
SD930,754.12 18,855.63 455.23 415.77 141.45
Min111,788.00 2850.00 139.00 157.00 17.00
Max7,381,078.00 120,918.00 2016.00 2769.00 1131.00
2011Mean1,091,901.15 31,257.56 799.17 779.91 172.35
SD744,620.69 2161.58 553.31 600.63 154.24
Min111,287.00 3448.00 111.00 144.00 26.00
Max4,132,915.00 129,426.00 3408.00 4596.00 1196.00
2012Mean1,141,418.03 35,452.37 776.38 835.05 187.45
SD729,038.34 26,541.61 488.28 640.10 183.01
Min122,637.00 3173.00 81.00 141.00 23.00
Max3,780,418.00 161,004.00 2712.00 4677.00 1497.00
2013Mean1,169,242.55 37037.21 789.40 829.48 197.62
SD732,494.83 28,594.02 492.72 519.22 198.05
Min99,575.00 4113.00 113.00 156.00 20.00
Max3,802,118.00 198,410.00 2908.00 3353.00 1657.00
2014Mean1,138,600.08 36,502.15 801.26 900.16 216.56
SD695,198.60 29,741.37 477.59 573.65 196.28
Min88,394.00 3924.00 142.00 165.00 22.00
Max3,770,194.00 210,175.00 2947.00 3557.00 1544.00
2015Mean1,125,796.98 38,390.83 850.12 1005.55 236.78
SD676,580.06 30,452.48 543.67 668.92 229.35
Min79,844.00 3,138.00 137.00 169.00 25.00
Max3,294,833.00 206,940.00 3032.00 3775.00 1886.00
2016Mean1,130,715.92 40,448.90 882.20 1052.35 261.60
SD704,462.45 32,539.49 541.39 699.29 254.37
Min101,379.00 3489.00 118.00 206.00 21.00
Max3,564,319.00 214,586.00 3115.00 3900.00 2068.00
2017Mean1,095,871.95 41,745.28 909.79 1106.35 287.33
SD708,005.71 36,279.66 548.31 729.51 323.38
Min86,996.00 1473.00 137.00 180.00 36.00
Max3,311,100.00 247,733.00 3238.00 4090.00 2756.00
Table 3. The efficiency of Primary Health Care Institutions (PHCIs) in the counties of Hunan Province from 2009 to 2017 (input-oriented Banker, Charnes and Cooper (BCC) model).
Table 3. The efficiency of Primary Health Care Institutions (PHCIs) in the counties of Hunan Province from 2009 to 2017 (input-oriented Banker, Charnes and Cooper (BCC) model).
YearTE 1TEvrs 2SE 3
MeanNumber of Efficient Counties MeanNumber of Efficient CountiesMeanNumber of Efficient Counties
20090.5595(5.81%)0.69716(18.60%)0.8135(5.81%)
20100.6568(9.30%)0.76018(20.93%)0.8699(10.47%)
20110.6998(9.30%)0.78920(23.26%)0.8899(10.47%)
20120.73711(12.79%)0.79819(22.09%)0.92512(13.95%)
20130.74712(13.95%)0.81122(25.58%)0.92017(19.77%)
20140.75413(15.12%)0.81322(25.58%)0.92816(18.60%)
20150.75414(16.28%)0.81924(27.91%)0.92216(18.60%)
20160.6909(10.47%)0.78014(16.28%)0.8889(10.47%)
20170.73811(12.79%)0.82221(24.42%)0.89811(12.79%)
1 TE: Technical efficiency; 2 TEvrs: Technical efficiency from the variable return to scale Data Envelopment Analysis (VRS DEA); 3 SE: Scale efficiency = TE/TEvrs.
Table 4. Results of the Malmquist index model (input-oriented).
Table 4. Results of the Malmquist index model (input-oriented).
YearEFFCH 1TECHCH 2PECH 3SECH 4TFP 5
20101.186 0.766 1.103 1.075 0.909
20111.075 0.917 1.043 1.030 0.986
20121.061 0.995 1.014 1.046 1.055
20131.015 0.990 1.018 0.997 1.005
20141.010 0.908 1.005 1.005 0.917
20150.998 0.975 1.005 0.993 0.973
20160.910 1.072 0.948 0.960 0.976
20171.060 0.886 1.056 1.004 0.940
mean1.0370.9351.0231.0130.969
Frequency distribution
>166 (76.74%)0 (0.00%)60 (69.77%)60 (69.77%)24 (27.91%)
=11 (1.16%)0 (0.00%)10 (11.63%)5 (5.81%)1 (1.16%)
<119 (22.09%)86 (100.00%)16 (18.60%)21 (24.42%)61 (70.93%)
1 EFFCH: Technical efficiency change; 2 TECHCH: Technical change; 3 PECH: Pure technical efficiency change; 4 SECH: Scale efficiency change; 5 TFP: Total factor productivity change.
Table 5. Results of Tobit regression (N = 774).
Table 5. Results of Tobit regression (N = 774).
VariableCoefficientSET-Ratiop95% CI
Ln (the total population)0.1040.0147.36< 0.001 ***(0.076–0.132)
city level−0.4560.117−3.89< 0.001 ***(−0.686 to −0.226)
Ln (per capita GDP)0.0010.0190.030.977(−0.037–0.038)
the proportion of health technicians−0.4620.087−5.31< 0.001 ***(−0.633 to −0.291)
the proportion of beds−0.1810.058−3.120.002 **(−0.295 to −0.067)
Constant0.6990.1684.15< 0.001 ***(0.368–1.029)
Sigma0.2000.006 (0.189–0.211)
Log likelihood24.141
χ 2 77.750
Probability >   χ 2 < 0.001 ***
Note: *** p < 0.001; ** p < 0.01.

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Zhong, K.; Chen, L.; Cheng, S.; Chen, H.; Long, F. The Efficiency of Primary Health Care Institutions in the Counties of Hunan Province, China: Data from 2009 to 2017. Int. J. Environ. Res. Public Health 2020, 17, 1781. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051781

AMA Style

Zhong K, Chen L, Cheng S, Chen H, Long F. The Efficiency of Primary Health Care Institutions in the Counties of Hunan Province, China: Data from 2009 to 2017. International Journal of Environmental Research and Public Health. 2020; 17(5):1781. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051781

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Zhong, Kaili, Lv Chen, Sixiang Cheng, Hongjun Chen, and Fei Long. 2020. "The Efficiency of Primary Health Care Institutions in the Counties of Hunan Province, China: Data from 2009 to 2017" International Journal of Environmental Research and Public Health 17, no. 5: 1781. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051781

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