We analyze the changes in health human resources in ethnic areas of China before and after the implementation of the “Healthy China 2030” strategy and forecast its development trend to provide a basis and suggestions for optimizing the allocation of health human resources. Taking the release time of “Healthy China 2030” as a node, the Gini coefficient and agglomeration analysis method were used to measure the fair changes in health human resources allocation before and after the policy. GM(1,1) model was used to predict the development trend of health human resources in ethnic areas of China. Since the implementation of the “Healthy China 2030” strategy in 2016, the number of health human resources in ethnic areas in China has increased rapidly, with the number of medical technical personnel, licensed (assistant) doctors, and registered nurses per thousand population increasing by 24.08, 25.27 and 35. 74%, respectively. By the end of 2020, the Gini coefficient of various health human resources indicators in ethnic areas was < 0.2 (higher fairness), the HARD value of each index < 1 (lack of fairness), medical technical personnel's HARD/PAD > 1 (relatively fair), and license (assistant) doctors and registered nurses' HARD/PAD values < 1 (lack of fairness). The GM (1,1) model predicts that by 2030, the total number of medical technical personnel, licensed (assistant) doctors, and registered nurses in China's ethnic areas will increase to 1,446,346, 500,576, and 761,126 respectively. With the implementation of the “Healthy China 2030” strategy, the fairness of health human resources in ethnic areas of China has been improved, but the problem of insufficient allocation of health human resources according to geography and population still exists, and the improvement of fairness has encountered bottlenecks.
Research Article
Changes in Health Human Resources Allocation in Ethnic Areas of China Before and After Implementation of “Healthy China 2030” Strategy
https://doi.org/10.21203/rs.3.rs-2888828/v1
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health policy
health human resources
Gini coefficient
agglomeration degree
grey model
The World Health Organization has always advocated for its member countries to follow the basic principle of fair allocation of health resources [1] and encouraged to improve the level of regional medical and health services by promoting the fair allocation of regional health resources. Human resources are an important part of health resources, as health workers transform other health resources into health services through labor. [2]. The fairness of regional health human resource allocation has a profound impact on the overall level of regional health resource allocation.
China has a huge minority population and vast minority areas. For a long time, due to the influence of economic, geographical, cultural, historical, and other factors [3], health human resources are relatively scarce, and the accessibility of health services is insufficient. The Chinese government has always attached great importance to the development of health services in ethnic areas. At the economic level, the central government continues to expand the medical and health expenditures in ethnic minority areas using transfer payments [4] to promote the construction of local health infrastructure. At the institutional level, the local government dispatches health personnel to support and improve the local health service by building medical associations and medical communities [5]. At the social level, the government established private hospitals by encouraging the participation of enterprise capital in order to supplement the part that public health care failed to provide services [6]. Although these measures have achieved certain results, the development of health services in ethnic minority areas is still relatively slow due to factors such as lagging economic development, poor transportation environment, inconvenient language communication, and inadequate implementation of policies [7].
In 2016, the Chinese government officially implemented the “Healthy China 2030” strategy and issued the “Healthy China 2030” Planning Outline. This strategy not only focuses on improving the health level of the whole people but also explicitly emphasizes the fairness of health resource allocation in underdeveloped areas [8]. Since the implementation of this strategy, what is the current situation of health human resources allocation in ethnic areas of China? Has the fairness of health human resource allocation been improved? Where will the development of health human resources in ethnic areas go in the future? In order to give a scientific and objective answer to these questions, we choose the Gini coefficient and agglomeration degree as evaluation indexes, take the publication time of “Healthy China 2030” as a node, and analyze the fairness trend of health human resources allocation in ethnic areas of China before and after the publication of the policy from three dimensions: economy, geography, and population. By using use the GM(1,1) to predict the scale of health human resources by the end of 2030, we provide suggestions for the Chinese government to optimize the allocation of health human resources in ethnic areas.
A. Region division and data resources
Nationality is derived from “nationality”. In Chinese academic circles, ethnic areas are generally referred to as areas of the minority. From the perspective of administrative divisions, ethnic areas refer to the places where China has the power of ethnic autonomy. It includes all ethnic autonomous regions, autonomous prefectures, and autonomous counties (flags) such as Yunnan, Guizhou, and Qinghai provinces with a large minority population [9]. The ethnic areas refer to five ethnic autonomous regions: Inner Mongolia, Guangxi, Ningxia, Xinjiang, and Tibet.
We use the data of China's health human resources and social economy in the eastern, central, western, and ethnic areas from 2011 to 2020. In the division of the eastern, central, and western regions, we follow the definition of the China Statistical Yearbook. We excluded the provinces in ethnic areas from the western regions. The eastern region includes 11 provincial-level administrative regions including Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan, Beijing, Tianjin, and Shanghai. The central region includes 8 provincial administrative regions including Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan. There are 7 provincial administrative regions in the west, including Sichuan, Chongqing, Guizhou, Yunnan, Shaanxi, Gansu1, and Qinghai. Ethnic regions include 5 minority autonomous regions including Inner Mongolia, Guangxi, Tibet, Ningxia, and Xinjiang. The data on health human resources is collected from the China Health Statistics Yearbook published by the China National Health and Health Commission, and the data on social economy, population, and geography is collected from the China Statistics Yearbook published by the China National Bureau of Statistics. All the above data are public.
B. Measuring Tools
Gini coefficient is calculated by Corrado Gini according to the Lorenz curve as an important statistical index reflecting the fairness of social income distribution [10]. It is widely used in the fairness research of health resource allocation [11]. Equatoin (1) is for the calculation of Gini coefficient.
where Xi is the cumulative percentage of GDP in each region, and Yi is the cumulative percentage of health resources in each region. Gini coefficient less than 0.2 is the absolute average, 0.2‒0.3 is relatively average, 0.3‒0.4 is reasonable, 0.4‒0.5 is a big gap, and more than 0.5 is the disparity [12].
The concept of agglomeration degree rises in economics to indicate the concentration degree of a certain factor of production in a subordinate unit relative to that in a superior unit. Yuan and other scholars introduced the concept of agglomeration degree into the field of health [13]. The agglomeration degree of health human resources refers to the proportion of the total health human resources gathered in a certain area to the land area of 1% of all the evaluated areas [14] (Eq. (2)).
When HRADi > 1, the fairness of the geographical allocation of health human resources in this area is high. When HRADi = 1, the distribution of health human resources in this area is fair. When HRADi < 1, the fairness of the geographical allocation of health human resources in this area is low. The degree of population agglomeration refers to the proportion of the population in a certain area to the geographical area of 1% of the whole area [15] (Eq. (3)). PADi represents the population gathering value of a certain area, Pi represents the population of area i, Pn represents the total population of China, Ai represents the land area of area i and An represents the land area of China. Divide HARDi by PADi to obtain the resource allocation ratio from the perspective of population allocation.
When the ratio is greater than 1, the surplus of health human resources in an area. When the ratio is close to 1, the health human resource meets the needs of this area. When the ratio is less than 1, the shortage of health human resources in this area [16].
GM(1,1) model shows a dynamic system with quasi-differential equations. The process is mainly to create a first-order differential equation model and a fitting curve, and then predict the system. The differential equation is Eq. (4).
The GM(1,1) prediction model is established according to the number of health human resources in ethnic areas of China from 2011 to 2020 as the original sequence. The original sequence is expressed as Eq. (5). The time from 2011 to 2020 is assigned as k, k = 1,2, …, n. α and U are the undetermined coefficients. α is the endogenous control gray scale. X(0)(1) is the number of health human resources in China's ethnic areas in 2011, and x (1) (k + 1) is the cumulative predicted value. The predicted value x(0) (k + 1) can be finally obtained by Eq. (6).
A. Basic Situation of Health Human Resources Allocation in Ethnic Areas
By the end of 2020, there were 846,589 medical technical personnel in ethnic areas, accounting for 7.94% of the total number of medical technical personnel, 307,032 licensed (assistant) doctors, accounting for 7.51% of the total number of licensed (assistant) doctors, and 364,426 registered nurses, accounting for 7.74% of the total number of registered nurses in China. According to the index of health human resources per thousand population, the number of medical technical personnel, licensed (assistant) doctors, and registered nurses in minority areas in 2020 increased by 24.08, 24.77, and 35.54% respectively compared with that in 2015, but none of them reached the national growth level. Compared with the eastern, central, and western regions, the growth rate of various indicators in ethnic minority areas lags behind the eastern, central, and western regions, as shown in Table I.
| Area | Per thousand population Medical Technical Personnel/people | Per thousand population License (assistant) Doctors/person | Per thousand population Registered Nurse/person | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 2015 | 2020 | Growth rate(%) | 2015 | 2020 | Growth rate(%) | 2015 | 2020 | Growth rate (%) | |
| Whole country | 5.84 | 7.57 | 29.62 | 2.21 | 2.90 | 31.22 | 2.37 | 3.34 | 40.93 |
| Ethnic minority area | 6.15 | 7.63 | 24.07 | 2.22 | 2.77 | 24.77 | 2.42 | 3.28 | 35.54 |
| East area | 6.01 | 7.68 | 27.79 | 2.33 | 3.06 | 31.33 | 2.44 | 3.32 | 36.07 |
| Midland | 5.43 | 7.13 | 31.31 | 2.10 | 2.75 | 30.95 | 2.24 | 3.22 | 43.75 |
| Western area | 5.94 | 7.78 | 31.08 | 2.08 | 2.72 | 30.50 | 2.46 | 3.51 | 42.85 |
B. Equity evaluation of health resources allocation based on Gini
We calculated the Gini coefficient of the economic distribution of health human resources in ethnic areas from 2011 to 2020 (Fig. 1) and its changes in 2011‒2015 and 2016‒2020, respectively, with 2016 as the time node. From 2011 to 2015, the Gini coefficient of medical technical personnel, licensed (assistant) doctors, and registered nurses in ethnic areas showed negative growth, with the growth rates of -16.29, -17.37, and 19.60% respectively. Five years after the implementation of the “Healthy China” strategy, the Gini coefficients changed by -37.50, -57.60, and − 22.40%, respectively. The three values were all less than 0.2, which was in the absolute fair range. The order of fairness is licensed (assistant) doctors > medical technical personnel > registered nurses.
Compared with the change range from 2011 to 2015, the improvement of health human resources in ethnic areas after the implementation of the policy is obvious, and the policy effect is remarkable.
After 2019, the global economy went down, and the development of China's economy slowed down correspondingly. Therefore, at the end of 2020, the Gini coefficient increased compared with that in 2019 according to economic distribution. However, in the eastern, central, and western regions in the same economic environment, the Gini coefficients of various health human resources indicators in ethnic areas have dropped since the implementation of the “Healthy China” strategy as shown in Table II.
| Area | Types of health human resources | 2015 | 2020 | growth margin(%) |
|---|---|---|---|---|
| Ethnic minority area | Medical Technical Personnel | 0. 1376 | 0. 0825 | -40. 04 |
| License (Assistant) Doctors | 0. 1116 | 0. 0460 | -58. 78 | |
| Registered Nurse | 0. 1532 | 0. 1098 | -28. 33 | |
| East area | Medical Technical Personnel | 0. 1099 | 0. 1471 | 33. 85 |
| License (Assistant) Doctors | 0. 1257 | 0. 1725 | 37. 23 | |
| Registered Nurse | 0. 0960 | 0. 1334 | 38. 96 | |
| Midland | Medical Technical Personnel | 0. 0421 | 0. 0692 | 64. 37 |
| License (Assistant) Doctors | 0. 0604 | 0. 0803 | 32. 95 | |
| Registered Nurse | 0. 0285 | 0. 0557 | 95. 44 | |
| Western area | Medical Technical Personnel | 0. 0820 | 0. 0978 | 19. 27 |
| License (Assistant) Doctors | 0. 0972 | 0. 0886 | -8. 85 | |
| Registered Nurse | 0. 0772 | 0. 0979 | 26. 81 |
C. Evaluation of Fairness of Health Resource Allocation Based on Agglomeration Degree
By the end of 2020, the HARD values of various health human resources indicators in ethnic minority areas were < 1, and the fairness of health human resources in geographical configuration was relatively low (Table III). From 2016 to 2020, the HARD value of medical technical personnel decreased slightly and remained in the range of 0.17‒0.18. The HARD value of licensed (assistant) doctors and registered nurses remained at 0.17, and the fairness has not changed significantly. At the end of 2020, the HARD values of the three health human resources indicators are all 0.17.
| Year | PAD | Medical Technical Personnel | License (Assistant) Doctors | Registered Nurse | |||
|---|---|---|---|---|---|---|---|
| HARD | HARD/PAD | HARD | HARD/PAD | HARD | HARD/PAD | ||
| 2011 | 0.17 | 0.18 | 1.07 | 0.18 | 1.05 | 0.18 | 1.06 |
| 2012 | 0.17 | 0.18 | 1.06 | 0.17 | 1.03 | 0.18 | 1.04 |
| 2013 | 0.17 | 0.18 | 1.06 | 0.17 | 1.02 | 0.17 | 1.03 |
| 2014 | 0.17 | 0.18 | 1.06 | 0.17 | 1.01 | 0.17 | 1.04 |
| 2015 | 0.17 | 0.18 | 1.06 | 0.17 | 1.01 | 0.17 | 1.03 |
| 2016 | 0.17 | 0.18 | 1.06 | 0.17 | 1.00 | 0.17 | 1.03 |
| 2017 | 0.17 | 0.18 | 1.04 | 0.17 | 0.99 | 0.17 | 1.02 |
| 2018 | 0.17 | 0.17 | 1.02 | 0.17 | 0.96 | 0.17 | 1.00 |
| 2019 | 0.17 | 0.17 | 1.01 | 0.17 | 0.96 | 0.17 | 0.98 |
| 2020 | 0.17 | 0.17 | 1.01 | 0.17 | 0.96 | 0.17 | 0.98 |
In order to better observe the impact of the “Healthy China” strategy on the geographical and demographic dimensions of ethnic areas, we calculated the concentration of health human resources in the eastern, central, and western regions of China in 2015 and 2020. We used the horizontal comparison method to understand the effect of the policy comprehensively. Table IV shows that the change range of the agglomeration degree of health human resources in each region is relatively small, and all data changes are less than 0.15. The gap in health human resources among regions changes little, and the eastern region still maintains the advantage of resource allocation for each region. This shows that the strategy of “Healthy China” has little influence on the allocation of health human resources in geographical and demographic dimensions, and the effect is not obvious. However, considering the geographical characteristics of the backward ethnic areas, the result of the small change in fairness in the geographical dimension is more reasonable
| Area | Year | PAD | Medical Technical Personnel | License (Assistant) Doctors | Registered Nurse | |||
| HARD | HARD/PAD | HARD | HARD/PAD | HARD | HARD/PAD | |||
| Ethnic minority area | 2015 | 0.17 | 0.18 | 1.06 | 0.17 | 1.01 | 0.17 | 1.04 |
| 2020 | 0.17 | 0.17 | 1.01 | 0.17 | 0.96 | 0.17 | 0.98 | |
| Eastern area | 2015 | 4.04 | 4.22 | 1.04 | 4.28 | 1.06 | 4.26 | 1.05 |
| 2020 | 4.14 | 4.20 | 1.02 | 4.38 | 1.06 | 4.12 | 1.00 | |
| Midland | 2015 | 2.46 | 2.29 | 0.93 | 2.32 | 0.95 | 2.33 | 0.95 |
| 2020 | 2.42 | 2.28 | 0.94 | 2.30 | 0.95 | 2.34 | 0.97 | |
| Western area | 2015 | 0.27 | 0.26 | 0.96 | 0.25 | 0.91 | 0.25 | 0.94 |
| 2020 | 0.27 | 0.28 | 1.03 | 0.25 | 0.94 | 0.28 | 1.05 | |
D. Prediction of Health Human Resources in Ethnic Areas
According to the data on health human resources in ethnic areas from 2011 to 2020, GM(1,1) was established by using MATLAB R2016a software. The posterior difference ratio C value is less than 0.35, the small error probability P value is all greater than 0.95, and the model accuracy level is excellent at Grade 1, as shown in Table V.
| Types of health human resources | parameter value | C | P |
|---|---|---|---|
| Medical Technical Personnel | =-0. 0534 = 510860. 8895 | 0. 0025 | 1 |
| License (Assistant) Doctors | =-0. 05 = 188716. 2926 | 0. 005 | 1 |
| Registered Nurse | =-0. 0728 =184723. 2619 | 0. 0028 | 1 |
Based on the data of medical technical personnel, the GM (1,1) prediction model was used for a prediction of the number of licensed (assistant) doctors and registered nurses in ethnic areas from 2011 to 2020 in China Health Statistics Yearbook. The results are shown in Table VI. Among them, the average annual growth rate of health technical personnel is 5.49%, the average annual growth rate of licensed (assistant) physicians is 5.12%, and the average annual growth rate of registered nurses is 7.55%.
| Year | Medical Technical Personnel /people | License (Assistant) Doctors /person | Registered Nurse /person |
|---|---|---|---|
| 2021 | 894089 | 319260 | 395314 |
| 2022 | 943172 | 335620 | 425163 |
| 2023 | 994950 | 352817 | 457265 |
| 2024 | 1049570 | 370897 | 491791 |
| 2025 | 1107188 | 389902 | 528924 |
| 2026 | 1167970 | 409882 | 568860 |
| 2027 | 1232088 | 430885 | 611813 |
| 2028 | 1299726 | 452965 | 658008 |
| 2029 | 1371077 | 476176 | 707691 |
| 2030 | 1446346 | 500576 | 761126 |
We analyzed the changes of equity in health human resources allocation before and after the implementation of the “Healthy China 2030” strategy from the three dimensions of economy, geography, and population, and the total number of various human resources is predicted by the completion of the strategy using GM(1,1) model.
The results showed that in the economic dimension, the fairness of health human resource allocation in ethnic minority areas has been significantly improved, and the increase in fairness was significantly higher than that in the eastern, central, and western regions.
In the geographical dimension, the fairness of the allocation of health human resources has not changed significantly. It is still in the unfair range, and the gap with the eastern, central, and western regions has not been narrowed. According to research based on local conditions, by the end of 2020, the geographical area of ethnic minority areas accounted for 45.63% of China's total territorial area, but the number of the population accounted for 7.87% of China's total population. The population density per square kilometer on average was less than 4,000, far lower than that in the central and eastern regions. The local area is sparsely populated. Therefore, under such geographical conditions, the lack of local health human resources is reasonable to some extent.
In population, except that the medical technical personnel indicators were within the fair range, neither the licensed (assistant) doctors nor the registered nurse indicators reached the fair range, and the fairness of various health manpower indicators continued the downward trend since 2011.
Table I shows that the number of health human resources per thousand population in ethnic areas has continued to grow. The prediction in Table VI presents that the health human resources in ethnic areas continue to increase in the next decade. Therefore, the improvement of fairness in the allocation of health human resources in ethnic areas is worth looking forward to.
China's ethnic areas have vast underdeveloped rural areas [17,18], which have been relatively behind economic development for a long time. In order to change this situation, in 2015, the Chinese government officially released the “Decision on Winning the Fight Against Poverty”, a policy aimed at helping more than 700 million poor people in rural China to get out of poverty. In 2018, the Chinese government issued the “Guidance on Winning the Three-Year Action to Fight against Poverty”. The introduction of this policy pushed China's efforts, breadth, depth, and accuracy in the fight against poverty to a new level [19]. Through the continuous efforts of the Chinese government, the number of poverty-stricken areas and population in China continued decreasing after 2015. The economic development of ethnic minority areas has also been greatly improved. This objectively explains the reason why the Gini coefficient of health human resources in ethnic areas dropped significantly and the fairness has improved obviously.
In 2017, the Chinese government implemented a “rural revitalization” strategy to speed up rural construction. This strategy attracted a large number of enterprises and foreign capital to invest and set up factories [20]. In rural construction, a large number of young people returned to the countryside, which led to the declining fairness of local health human resources according to population distribution. Especially after 2016, this downward trend was more obvious than that in 2011‒2015 as shown in Table III. With the support of relevant government strategies and policies, the economic development in ethnic areas has been successful, but the construction of health human resources is still insufficient. During the ten years from 2011 to 2020, the three indicators of medical technical personnel, licensed (assistant) doctors, and registered nurses were around the range of 0.17‒0.18, which indicated that the implementation of the “Healthy China 2030” strategy has not played a significant role in improving the equity of health manpower. Through investigation and research, poor treatment of health personnel, the insufficient guarantee of government funds, and difficulty in implementing relevant personnel policies [21] are the important reasons for the loss of health personnel and the slow development and construction of health personnel. It is also the cause of the long-term lack of equity in the geographical and demographic dimensions of health human resources in ethnic areas.
According to the prediction results of the GM(1,1) model, the number of medical technical personnel will increase year by year from 2021 to 2030 [22], which is related to the continuous promotion of the “Healthy China 2030” strategy [23]. In recent years, medical education in ethnic areas has developed rapidly, the enrollment scale has been continuously expanded, and the number of basic newly-built medical and health institutions has been increasing, which provides the development of health personnel.
Since the new medical reform in 2009, the Chinese government has paid more attention to health problems. The “Healthy China 2030” strategy is the embodiment of the Chinese government's determination to improve the health level of the Chinese people. The “Healthy China 2030” strategy is a scientific and reasonable strategy. However, it is extremely difficult to improve the health level of an area with a long-term underdeveloped economy and health construction.
With the time of the “Healthy China 2030” strategy as the node and the panel data of ten years (2011‒2020) in ethnic areas, we analyzed the impact of this strategy on the changes in health human resources in ethnic areas in China and predicted its development trend. The research results present significance for the Chinese government in the development and construction of health human resources in ethnic areas. The fairness of ethnic minority areas has been improved in general, especially in the economic dimension. However, in the geographical and demographic dimensions, fairness is still relatively weak, and the policy effect is unsatisfactory. The loss of health personnel is the fundamental reason why the improvement of the fairness of health human resources in ethnic minority areas in the geographical and demographic dimensions has been difficult.
The lack of local government guarantee on the treatment of health personnel and the funds for team building, as well as the inadequate implementation of relevant personnel policies are the important reasons for the loss of personnel [24]. However, the construction of related funds and personnel treatment is explicitly required in the “Healthy China 2030” Planning Outline. The local government has insufficient understanding and implementation of the strategy. The prediction results of the GM(1,1) model show that the scale of health human resources in ethnic areas continues to expand in the next decade, but how to ensure the non-loss of health personnel is the problem to solve.
Local governments must implement the strategic requirements of "Healthy China 2030". In the financial expansion of health expenditure, the "low not high" performance wage policy needs to be improved to establish a compensation system in line with the local reality and the basic treatment of health personnel. In terms of policy, supporting policies must be proposed in line with local development needs in line with local conditions to ensure that the strategy can be fully implemented in the local, faster break through the bottleneck period of policy implementation. At the institutional level, the local government needs to improve the system of talent protection and introduction and innovate the personnel employment mechanism and management system. Exploring the new modes of “medical integration”, “medical community”, “county recruit township”, and “county and township integration” promotes the improvement of the local health service level.
Acknowledgment
We appreciate the effort of Department of Public Health in Guangxi university of Chinese Medicine and Doctor Wang. Thanks for your assistance and contribution.
Author Contributions
Conceptualization: Hailong Fan.
Data curation: Hailong Fan, Junbao Shi,Yanan Wang.
Formal analysis: Hailong Fan.
Writing-original draft: Hailong Fan, Junbao Shi.
Writing-review & editing: Biyan Wang.
Corresponding author
Correspondence to Biyan Wang.
Funding
The authors received no specific funding for this work.
Ethics declarations
Ethical approval and consent to participate
Not Applicable.
Consent for publication
Not Applicable.
Competing interests
The author has no competing interest.
Data Availability Statement
The data for this study are available at China Health StatisticsYearbook (http://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/new_list.shtml).
Copyright
© 2023 Fan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- World Health Organization. Halth Topics About Health Equity. (2020). Available online at: http://www. who. int/topics/health_equity/en/ (accessed November 23, 2020).
- Sun Mei. Research on Problems and Strategies of Health Human Resources in China [M]. Beijing: Peking Union Medical College Press, October 2021.
- Yazhi, Fang Jingwen. The development of medical and health undertakings in ethnic areas since the founding of New China [J]. Party History Expo, 2022(02):33.
- Luo F, Zou P, Zhang H, Pang S. Exploration of village health centres in Northern and Central China: A qualitative study. Aust J Rural Health. 2020 Jun;28(3):271-280. doi: 10. 1111/ajr. 12635. Epub 2020 Jun 18. PMID: 32558026.
- Hu D, Zhu W, Fu Y, Zhang M, Zhao Y, Hanson K, Martinez-Alvarez M, Liu X. Development of village doctors in China: financial compensation and health system support. Int J Equity Health. 2017 Jul 1;16(1):9. doi: 10. 1186/s12939-016-0505-7. PMID: 28666444; PMCID: PMC5493879.
- Chen Ling, Wu Yingmin, Li Jiawei, Zhu Liyan. Research on the current situation and efficiency of informatization construction of primary medical and health institutions in ethnic areas [J]. China General Practice, 2022,25(16):2003-2007+2020.
- Zheng Yong. Dilemma of the rule of law in the development of medical and health undertakings in ethnic areas and its countermeasures [J]. Guizhou Ethnic Studies, 2018,39 (03): 31-36. doi: 10. 13965/j. cnki. gzmzyj10026959. 2018. 03. 008.
- The CPC Central Committee and the State Council issued the Outline of the “Healthy China 2030” Plan [J]. Bulletin of the State Council of the People's Republic of China, 2016(32):5-20.
- Li Ming. Research on the Construction of Local Curriculum of Teacher Education in Ethnic Areas in the Context of the Chinese National Community [D]. Southwest University, 2021. doi: 10. 27684/d. cnki. gxndx. 20084. 00000000084
- Druckman A, Jackson T. Measuring resource inequalities: the concepts and methodology for an area-based Gini coefficient. Ecol Econ. (2008) 65:242–52. doi: 10. 1016/j. ecolecon. 2007. 12. 013
- Zhu Bin, Mao Ying, He Rongxin, Zhang Ning, Ning Wei, Liu Jinlin. Survey of measurement methods and application of fairness in allocation of health human resources in China [J]. chinese health service management, 2022,39(01):10-16.
- Ray, Xu Changsheng. Comparative study on spatial allocation of health resources in Henan province before and after the new medical reform [J]. chinese health economics, 2016,35 (7): 51-52.
- Yuan Suwei, Wei Fengqing, Liu Wenwei, Zhang Zhe, Ma Jin. Methodological Discussion on Evaluating the Equity of Health Resource Allocation by Agglomeration [J]. China Hospital Management, 2015,35(02):3-5.
- Liu Wenwei, Wei Fengqing, Yuan Suwei, Zhang Zhe, Ma Jin. Evaluation of urban and rural health human resource planning in China based on aggregation degree [J]. China Hospital Management, 2015,35(03):1-4.
- Wang Yijin, He Renfei. Panel data analysis of county population agglomeration and economic agglomeration —— based on the empirical study of Beibu Gulf urban agglomeration [J]. Anhui Agricultural Science, 2014,42(25):8778-8782.
- Sun Yanan, Li Yuan, Ding Liangcheng, Li Jingyu, Gong Jing, Ning Pei, Meng Qinghui. Analysis on the current situation and fairness of nursing human resource allocation in Shandong province based on concentration degree and concentration index [J]. Modern Preventive Medicine, 2021,48(18):3348-3352+3420.
- Han Rong. Thoughts on boosting rural revitalization by industrial revitalization-taking Inner Mongolia Autonomous Region as an example [J]. Shanxi Agricultural Economics, 2022(6):50-52
- Cai Luming, Zhong Yu. Study on the coupling and coordination relationship between new urbanization and rural revitalization in ethnic areas-taking Guangxi Zhuang Autonomous Region as an example [J]. Regional Finance Research, 2021(4):87-92
- Yin Chengjie. Study on the long-term mechanism and policy of consolidating and expanding the achievements of poverty alleviation and rural revitalization [J]. Journal of Huazhong Normal University: Humanities and Social Sciences Edition, 2022,61(1):25-30
- Liu Heguang. Strategic key points and paths of rural revitalization [J]. china national conditions and strength, 2017,0(12):35-37
- Deng Meng, Luo Xuanliang, Huang Danqi, Qin Xianjing, Xu Tingting, Feng Qiming, Li Chen. Analysis on the allocation of primary health human resources in western China from 2015 to 2019 [J]. Guangxi Medicine, 2022,44(3):319-324
- Liang Binghua, Huang Lifeng. Forecast and analysis of medical and health resources in China based on GM(1,1) grey forecasting model [J]. Modern Preventive Medicine, 2021,48(20):3655-3659.
- Chang Liang. Study on the spatio-temporal pattern and influencing factors of healthy production efficiency of medical and health resources in China [D]. Huazhong Normal University, 2020. doi: 10. 27159/D. CNKI. GHZ SU. 20010. 00000000015
- Zhou Hui, Fei Yu, Miao Tao, Chen Danqi. Evaluation of health resource allocation and utilization efficiency of primary health care institutions in western China from 2010 to 2019 [J]. Health Soft Science, 2022,36(05):58-61.
No competing interests reported.