
==== Front
BMC Public Health
BMC Public Health
BMC Public Health
1471-2458
BioMed Central London

19905
10.1186/s12889-024-19905-9
Research
Spatial disparities and dynamic evolution of professional public health resource supply level in Beijing, China
Wu Rui
Gesang Danzhen
Zhou Guangxin
Li Ying liyingccmu@126.com

https://ror.org/013xs5b60 grid.24696.3f 0000 0004 0369 153X School of Public Health, Capital Medical University, Beijing, China
17 9 2024
17 9 2024
2024
24 252413 6 2024
27 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

This study aims to explore the development status of the supply level of professional public health resources in Beijing Municipality, analyze the areal differences and spatial distribution characteristics of the supply level in 16 districts, and provide a scientific basis for promoting the balanced development of the supply level of professional public health resources in each district of Beijing Municipality.

Methods

Based on panel data from Statistical Yearbook of Health Work in Beijing Municipality and Health and Family Planning Work in Beijing Municipality from 2014 to 2022. Using the entropy method to measure the supply level of professional public health resources in Beijing, employing the Dagum Gini coefficient and Kernel density estimation method to analyze the spatial differentiation characteristics and dynamic evolution process of the supply level, and using heat maps to display the spatial distribution of the supply level in various districts of Beijing.

Results

The Dagum Gini coefficient of the supply level of professional public health resources in Beijing Municipality decreased continuously from 0.3419 in 2014 to 0.29736 in 2020, then gradually increased, showing a trend of initially decreasing and then increasing overall spatial differences. The spatial differences mainly stem from differences between areas. The kernel density curve shows that the supply level of professional public health resources in Beijing Municipality gradually increased, slightly decreased after 2021, and did not form a situation of two or multi-level differentiation.

Conclusion

From 2014 to 2022, the supply level of professional public health resources in Beijing Municipality showed an overall upward trend, but attention should be paid to the decline after 2021; spatial differences initially decreased and then increased, and the differences between areas is the main source of the overall difference in Beijing. Therefore, the Beijing Municipal Government should focus on narrowing the differences between areas, determine the allocation and management of public health resources based on the actual situation of core areas, promote coordinated development within and outside areas, and thus enhance the supply level of professional public health resources.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-024-19905-9.

Keywords

Professional public health resources
Spatial disparities
Dynamic evolution
Dagum Gini coefficient
Kernel density estimation
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Public health is a public service that safeguards and promotes the health of the population [1], and it is the cornerstone of building a Healthy China [2]. Professional public health resources refer to the government's collection of funds and use of financial means to provide professional public health institutions with the human, material, and financial resources they need, in order to ensure that all residents have free or low-cost access to health resources that serve the cause of human health, possessing the pure public good nature. [3].The equity of resource supply is the foundation of the construction of the public health system. From the perspective of residents' daily lives, an adequate supply of professional public health resources can provide residents with preventive care, health education, disease screening, and other services, helping residents establish healthy lifestyles, reduce health risks, and improve their quality of life. From the perspective of public safety in society, in the event of infectious disease epidemics, natural disasters emergencies, professional public health resources enable public health institutions to respond quickly, provide timely and effective medical treatment and health epidemic prevention services, enhance the ability to resist public health risks, and safeguard the lives and health of the public. In recent years, the Chinese government has made significant investments in medical resources [4], achieving many accomplishments in promoting the construction and development of basic medical service systems. However, improvements at the national level have masked the persistent disparities at the local level [5, 6]. The critical issue of constraints on the development of public health has not been thoroughly addressed. The public health sector continues to exhibit disparities in distribution and underutilization of resources in various regions [7]. The unequal distribution of health resources is closely linked to the widening gap in health outcomes [8–10], greatly limiting the sound development of the health sector in China.

As the capital city with relatively abundant medical resources in China, the level of public health services in Beijing is not only directly related to the health and well-being of residents, but also serves as an important window to showcase the construction of the national public health system. Therefore, in-depth research on the supply level of professional public health resources in Beijing, revealing differences in resource allocation and their reasons, is of strategic significance for forming public health models that can be learned from and replicated, leading the enhancement of the national public health service level. This study focuses on the level of supply of professional public health resources and does not include the dimension of demand. The reasons are as follows: First, from a policy perspective, the supply of professional public health resources is a key focus of future medical and health work in Beijing. The Special Plan for Medical and Health Facilities in Beijing (2020–2035) points out that the infrastructure conditions of public health units are relatively weak, such as maternal and child health service institutions and disease control centers in Beijing, and the public health service system is in urgent need of improvement [11]. Second, based on existing research, the insufficient demand for professional public health resources is a consensus issue in academia. Through reviewing research literature on the allocation of health resources such as healthcare personnel and bed facilities, we understand that in most regions, the demand for health resources exceeds the supply. Currently, the issues of personnel shortage and insufficient bed capacity have not been thoroughly addressed [12–14]. Third, the outbreak of the COVID-19 pandemic poses greater challenges to the public health supply of internationalized metropolises. Beijing, as an internationalized metropolis, characterized by frequent international exchanges, high population density, and rapidly changing lifestyles, faces an increasing demand for services such as chronic disease management and infectious disease control provided by professional public health institutions [15]. Fourth, due to the fiscal decentralization system implemented in China, each province has a certain degree of autonomy in the allocation and utilization of financial resources. Governments at all levels (provincial, municipal, county) formulate and implement financial input policies based on local conditions. This arrangement results in significant differences in the supply level of professional public health resources between different provinces and within different regions of the same province. While studies on the supply level of professional public health resources have been conducted at the national level, they often focus on overall trends and average levels, failing to fully reflect the diversity among regions within each province. There is a need for targeted and in-depth exploration of the supply levels at the regional level. Based on the four reasons mentioned above, we focused on analyzing the supply of professional public health resources, understanding the differences in the allocation of public health resources in different areas of Beijing, exploring the effectiveness and adaptability of policy implementation. We hope to help decision-makers identify resource gaps in each district, provide a reference basis for the overall planning and resource optimization of Beijing in the future, and better meet the needs of the public.

Literature review

In China, medical and health institutions are divided into hospitals, primary medical and health institutions, professional public health institutions, and other medical and health institutions. Medical and health resources refer to the combination of all health institution resources. Domestic experts and scholars have actively explored resource allocation issues for all or some institutions, and research on a national scale is relatively abundant. Research by scholars such as Zhu (2018) and Zhang (2021) indicates that prior to the new healthcare reform, one of the most significant issues faced by the Chinese healthcare system was the inadequate and unbalanced allocation of health resources. Disparities in the population and geographical distribution of medical resources are considered a prominent healthcare issue in China [16–18]. Pan (2021) applied agglomeration to study the equity of health resource allocation in China, proposing that the fairness of health resource allocation by geographical area in China is as follows: the eastern region > the central region > the western region [19]. Zhang (2019) analyzed the equity of the allocation of professional public health institutions in China from 2012 to 2016 using the Gini coefficient and the Theil index. The results indicate an uneven regional distribution, highlighting the ongoing need to continually increase the quantity of public health resources, particularly in addressing the issue of inadequate nurse staffing [20]. Wu 's study in 2023 demonstrates that there is still a significant gap in the overall allocation of human resources, with continuous differentiation in the allocation of health human resources among professional public health institutions across regions [21]. Zhou (2023) analyzed the spatial differences and dynamic evolution process of the supply level of professional public health resources in China, finding that from 2012 to 2020, the supply level of professional public health resources in China has increased, with the overall disparity initially slowly increasing and then gradually decreasing [22]. Li (2024) explored the spatiotemporal distribution, regional disparities, and convergence of public health levels at the provincial level in China. The study found that the public health level in China is generally low, with prominent structural contradictions, and the construction of a healthy environment has become a bottleneck hindering the improvement of public health levels in China [23]. Song's (2024) research reveals that despite overall advancements in China's medical resources, temporal and spatial disparities persist at the county level, necessitating the resolution of inequalities at the local level [24].

The research on Beijing's health resources mainly focuses on the supply of resources in primary healthcare institutions. Wang (2019) utilized calculations based on the Gini coefficient, coefficient of variation to demonstrate that the equity in the allocation of various health resources in Beijing and Shanghai has been improving gradually, with a reduction in disparities [25]. Zheng et al. (2020) evaluated the resource allocation situation and equity of primary healthcare institutions in Beijing using the Lorenz curve and Gini coefficient methods, revealing that population-based resource allocation equity is better than geographic distribution [26]. Huang (2022) investigated the development status of primary healthcare institutions in a district of Beijing under the tiered diagnosis and treatment system, showing that the distribution of outpatient and inpatient technical resources in primary healthcare institutions is uneven, requiring urgent adjustments in personnel structure. These institutions have not fully achieved the goals set by the state in terms of disease rehabilitation, nursing services, and other functions [27].

Overall, domestic scholars have provided a reliable theoretical basis for this article's research on health resources. However, there are still two aspects that need to be considered: First, in addition to professional public health institutions, other medical and health institutions have also established public health departments and provide certain public health services. However, they are not the main professional providers of public health services. Research on public health resources has not completely differentiated between the general level and the professional level of resource provision, and has not emphasized the uniqueness of professional public health institutions. Second, while health resources in Beijing have received much attention, there is a greater focus on research on primary medical and health service institutions, with a lack of research on professional public health resources.

In foreign studies, some scholars have revealed the problems existing in the allocation of resources in the health sector. Townsend (1974) first confirmed the imbalance and inequity in the allocation of health services [28]. Donna M. Meagher-Stewart (2007) argues that health equity is a key factor in improving and maintaining population health. Through a study of public health practices in Nova Scotia, Canada, they identified factors hindering the public health system, including limited information and knowledge systems, a lack of public health workers with graduate-level training, and insufficient funding [29]. Research by Vivian Valdmanis (2014) indicates an excess capacity in direct care services at public health centers in Florida. Public policy decisions regarding public health suggest that resource allocation in Florida may be more suited to traditional public health initiatives [30]. Bernadette Pauly and colleagues (2013) believe that efforts should be made to establish a robust public health system to support public health providers with the knowledge, skills, and resources needed to promote health equity and reduce disparities in population health [31]. McCullough JM (2019) demonstrates that government investments in health, social services, and education have a positive impact on community health outcomes [32]. Libert (2020) points out in his research on health equity in public health practice that challenges facing the healthcare system include the need to increase diversity among public health and medical professionals [33]. Gulzar H. Shah (2016) believes that cross-jurisdictional resource sharing is a crucial opportunity to further enhance the efficiency and benefits of public health resources, and it is a feasible and commonly used process to address new and emerging challenges in public health, advocating for increased resource sharing to tackle emerging challenges [34]. Erwin J. Delgado (2022) proposed a nonlinear programming model to address this problem, which is based on the use of the Gini index coverage formula to measure fairness and accessibility, also considering simulation algorithms to generate problem instances, and conducting computational experiments to demonstrate the potential utility of the proposed mathematical planning model, suggesting that the allocation of resources provided by medical centers be included in the decision-making process [35].

In summary, foreign scholars have conducted extensive research on the disparities and equity in healthcare services. The majority of these studies emphasize the importance of healthcare resources in developing public health systems and improving people's health levels. However, research on the supply levels of public health resources and regional disparities is not as common.

Based on the current research both domestically and internationally, existing studies lack a comprehensive analysis of the allocation status and equity of professional public health resources in Beijing. With the increasingly evident effects of the healthcare reform in Beijing, as the capital city with relatively abundant medical and health resources, the level of supply of professional public health resources has become a focal point of attention from various sectors of society. This article measures the supply level of professional public health resources in 16 districts of Beijing from 2014 to 2022 using the entropy weight method. It then conducts a detailed analysis of the sources of differences in supply level and spatial evolutionary trends through the Dagum Gini coefficient and Kernel density estimation (KDE) methods, providing decision-making basis for government departments to formulate public health resource allocation plans, promoting the improvement of the supply level of public health resources.

Materials and methods

Data sources

According to the definition of the statistical caliber in the yearbook, professional public health institutions include disease control centers, maternal and child health care, family planning technical services, health education, specialized disease prevention and treatment, blood collection and supply, health supervision, and emergency centers [36]. This study selected the period from 2014 to 2022 as the research period, using panel data from 16 professional public health institutions in the 16 districts of Beijing from 2014 to 2022 as the research sample. According to administrative division, the 16 districts of Beijing are divided into three areas: Core area (6 districts): Dongcheng, Xicheng, Chaoyang, Haidian, Fengtai, and Shijingshan. Suburban area (6 districts): Daxing, Tongzhou, Shunyi, Changping, Mentougou, and Fangshan. Outlying area (4 districts): Huairou, Pinggu, Miyun, and Yanqing [37].All research data comes from the Beijing Health Work Statistics and Beijing Health and Family Planning Work statistical yearbooks [38].

This study selects evaluation indicators for the supply level of professional public health resources as shown in Table 1. The reasons for choosing these three indicators are as follows: First, policy documents and annual reports indicate that these indicators are highly representative when assessing and analyzing the quantity of health resources. In 2015, the General Office of the State Council of China issued the "National Health Service System Planning Outline (2015–2020)", which designated indicators such as the number of medical and health institution beds per thousand permanent residents, the number of public health personnel per thousand permanent residents, hospital quantity, and the quantity of primary medical and health institutions as guiding indicators for the allocation of national health service system resources [39]. Second, in the annual national economic and social development statistical bulletins released by various provinces (direct-controlled municipalities and autonomous regions) in China, the data in the health report mainly includes the number of medical institutions, beds, and health technical personnel. Third, through literature review, we found that many studies consider the number of institutions, beds, and health technical personnel as indicators for evaluating the level of health resources [20, 22, 23, 40], and they also serve as input indicators for measuring fairness in health care resource allocation efficiency [41, 42]. In conclusion, based on data availability and representativeness, the number of institutions, beds, and personnel reserves of professional public health institutions in Beijing can be used as the main statistical indicators to measure the supply level of professional public health resources. Table 1 The evaluation indicators for the supply level of professional public health resources in this study

Evaluation indicator	Attribute	Weight	
Number of professional public health institutions per 10,000 people	 + 	0.3882	
Number of beds in professional public health institution per 10,000 people	 + 	0.3161	
Number of technicians in professional public health per 10,000 people	 + 	0.2956	
The weights of the indicators are calculated using the entropy method

Study design

This study was divided into three steps.

Calculation of supply level

For the first step, we utilized the entropy method to measure the supply level of professional public health resources in 16 districts of Beijing from 2014 to 2022. The entropy method determines the weights based on the amount of information contained in each indicator, where indicators with greater dispersion have a larger impact on the comprehensive evaluation. Therefore, the weight values calculated in this study can reflect the contribution of each indicator in the comprehensive evaluation index system of the supply level of professional public health resources in Beijing [43]. Due to the inconsistent dimensions of the indicators, it is necessary to perform dimensionless processing on these three indicators before the comprehensive evaluation [44]. The specific calculation steps [45] are as follows. The formulas are given by Eq. (1).

Build the target decision matrix. There are m samples and n evaluation indicators, forming the matrix X as follows:1 X=xij′m×n

In Eq. (1), xij′ represents the i-th area and the j-th indicator, i∈[1,m]，j∈[1,n].

Standardize and shift the indicators. Due to the inconsistencies in type and dimension among the three evaluation indicators, in order to eliminate the impact of these differences and ensure effective data processing, it is necessary to dimensionless the evaluation indicators and handle zero and negative values after standardization. Therefore, it is necessary to overall shift the dimensionless data, which can be calculated as follows:2 xij*=xij′-minxij′maxxij′-minxij′(i=1,2,⋯,m;j=1,2,⋯,n)

3 xij=xij∗+a(a=0.0001)

In above equations, xij′ represents the actual observed value of the j-th indicator of the i-th evaluation unit, and xij∗ denotes the standardized value of the corresponding positive indicator of the evaluation unit. max(xij) is the maximum value of the j-th indicator, and minxij is the minimum value of the j-th indicator. xij is the value of the positive indicator of the evaluation unit after shifting.

Calculate the normalized weights Pij after standardization and shift for each indicator and the entropy value ej of the j-th indicator.4 Pij=xij∑i=1mxij(i=1,2,⋯,m;j=1,2,⋯,n)

5 ej=-1lnm×∑i=1mPij×lnPij(i=1,2,⋯,m;j=1,2,⋯,n)

Calculate the coefficient of variation vi for the j-th indicator6 vi=1-ej

Calculate the weight wj of each indicator.7 wj=vi/∑j=1nvi(j=1,2,⋯,n)

Calculate the comprehensive score si of the supply level in each district, where Si∈0,1. A larger si indicates a higher supply level, while a smaller si indicates a lower supply level.8 si=∑j=1nwjxij∗(j=1,2,⋯,n)

Measurement of the degree of inequality in the level of supply

For the second step, we use the Dagum Gini coefficient to analyze the areal differences and sources of differences in the supply levels of professional public health resources. As an indicator of measuring inequality, the Dagum Gini coefficient explores the sources of areal differences based on the Gini coefficient. Unlike the Theil index, which decomposes the sources of areal differences into intra-areal differences and inter-areal differences [46], the Dagum Gini coefficient not only calculates overall spatial differences but also decomposes total differences into intra-areal differences, inter-areal differences, and transvariation intensity [47]. This effectively addresses the issue that the Theil index cannot consider the distribution of sub-samples and the problem of overlap between samples, providing a better explanation of the sources of areal differences [48]. A larger value of the Dagum Gini coefficient in this study indicates a lower level of fairness in the supply of professional public health resources at the areal level [49]. The specific calculation steps are as follows [45].9 G=∑j-1k∑h=1k∑i=1j∑r=1h|yji-yhr|2n2y¯

In Eq. (9), G represents the overall Gini coefficient, with a larger value indicating greater disparity. n is the number of districts; y¯ represents the average level of professional public health resource supply in Beijing; k represents the number of areas divided; nj represents the number of districts contained in area j, nh represents the number of districts contained in area h; yji represents the supply level of district i in area j, yhr represents the supply level of district r in area h. Before decomposing the Dagum Gini coefficient, it is necessary to sort the average supply levels of each district:10 Y¯h≤⋯Y¯j≤⋯Y¯k

According to the Dagum Gini coefficient decomposition method, the Dagum Gini coefficient G can be decomposed into intra-areal difference contribution value Gw, inter-areal difference contribution value Gnb, and transvariation intensity contribution value Gt. The formulas are given by Eqs. (11, 12, 13, and 14):11 G=Gw+Gnb+Gt

12 Gw=∑j=1kGjjpjsj

13 Gnb=∑j=2k∑h=1j-1Gjh(pjsh+phsj)Djh

14 Gt=∑j=2k∑h=1j-1Gjh(pjsh+phsj)(1-Djh)

In the above equations, Gjj represents the Gini coefficient in areaj,pj=nj/n,denotes the proportion of the number of districts nj in area j to the sample size n,sj=njY¯j/nY¯, where Y¯j signifies the overall supply level of professional public health resources in area j. Gjh stands for the Gini coefficient between areas j and h. ph=nh/n, denotes the proportion of the number of districts nh in area h to the sample size n, sh=nhY¯h/nY¯,where Y¯h signifies the overall supply level of professional public health resources in area h. Djh indicates the relative influence between areas j and h, Djh=djh-pjhdjh+pjh, djh represents the subtraction in supply levels between areas j and h, and is calculated by the formula: djh=∫0∞dFj(y)∫0y(y-x)dFh(x), pjh is the super-variable first-order moment, and its calculation formula is: pjh=∫0∞dFh(y)∫0y(y-x)dFj(x), Fh and Fj respectively represent the cumulative density distribution functions of the supply levels in areas h and j.

Description of the dynamic evolution process of supply level

For the third step, we use the Kernel density curve to describe the dynamic evolution of the supply level, which is often used in spatial disequilibrium analysis, and it can depict the dynamic evolution of the data distribution pattern, location, degree of polarization, and other characteristics over time through the continuous density curve. In short, the Kernel density curve reflects the probability distribution of the values of random variables [50], the left and right shifts of the center of the curve indicate the decrease and increase of the supply level of professional public health resources in Beijing, the higher the peaks of the curve imply the trend of the concentration of the supply level, and the higher the number of peaks indicates the more pronounced polarization [51].The KDE calculation is presented as follows [52]:15 Fx=1nh∑i=1nK(xi-μh)

In Eq. (15), n represents the number of districts, xi is the value of i-th supply level indicators, μ represents the mean, h represents the bandwidth. K is a kernel density function and the Gaussian kernel was adopted in this paper.

Additionally, we utilized multiple heat maps to visually display the temporal and spatial variations in the supply level of professional public health resources in Beijing. Different colors represent different supply levels, with darker colors indicating higher supply levels and lighter colors indicating lower supply levels. This approach allows for a more intuitive understanding of the spatial distribution and clustering of professional public health resource supply in Beijing, aiding in a better comprehension and analysis of the data. (Refer to the appendix for visualization).

Results

The overall situation of supply level

Using the entropy value method and the comprehensive weighting method, the weights of the three indicators, namely the number of professional public health institutions per 10,000 people, the number of beds in professional public health institutions per 10,000 people, and the number of professional public health technicians per 10,000 people, are calculated to be 0.3882, 0.31612, and 0.2956, respectively. The comprehensive scores and the average value of the level of the supply of professional public health resources for the 16 districts in Beijing in the period of 2014–2022 are derived from the calculations as shown in Table 2, the higher the comprehensive score indicates the higher the supply level. Table 2 Supply level of professional public health resources in 16 districts of Beijing (2014–2022)

Districts	Year	Mean	Rank	
2014	2015	2016	2017	2018	2019	2020	2021	2022	
Miyun	0.685	0.672	0.702	0.690	0.472	0.419	0.410	0.412	0.412	0.542	1	
Xicheng	0.454	0.433	0.456	0.523	0.549	0.563	0.597	0.570	0.585	0.525	2	
Pinggu	0.519	0.520	0.535	0.523	0.510	0.508	0.494	0.508	0.508	0.514	3	
Dongcheng	0.506	0.466	0.464	0.486	0.511	0.537	0.484	0.494	0.529	0.497	4	
Huairou	0.483	0.473	0.462	0.449	0.492	0.481	0.456	0.508	0.519	0.480	5	
Yanqing	0.469	0.495	0.495	0.474	0.459	0.451	0.437	0.448	0.457	0.465	6	
Mentougou	0.536	0.507	0.508	0.408	0.395	0.373	0.350	0.358	0.376	0.424	7	
Shunyi	0.376	0.430	0.387	0.376	0.359	0.338	0.319	0.320	0.248	0.350	8	
Fangshan	0.316	0.319	0.287	0.275	0.284	0.266	0.248	0.254	0.237	0.276	9	
Shijingshan	0.146	0.220	0.231	0.242	0.253	0.290	0.310	0.306	0.312	0.256	10	
Tongzhou	0.292	0.297	0.291	0.275	0.256	0.233	0.214	0.216	0.179	0.250	11	
Haidian	0.181	0.170	0.185	0.192	0.203	0.216	0.236	0.223	0.207	0.202	12	
Changping	0.112	0.101	0.082	0.065	0.062	0.070	0.065	0.078	0.069	0.078	13	
Daxing	0.094	0.084	0.081	0.077	0.073	0.057	0.073	0.083	0.082	0.078	14	
Chaoyang	0.063	0.063	0.062	0.071	0.077	0.079	0.105	0.087	0.087	0.077	15	
Fengtai	0.018	0.010	0.035	0.039	0.037	0.109	0.120	0.123	0.115	0.067	16	

According to the ranking of the supply level of professional public health resources in various districts, from 2014 to 2022, Miyun, Xicheng, and Pinggu ranked in the top three in comprehensive scores, with average supply levels of 0.542, 0.525, and 0.514 respectively. On the other hand, Daxing, Chaoyang, and Fengtai ranked in the bottom three, with average supply levels of 0.078, 0.077, and 0.067 respectively.

According to the supply level scores of the three areas: core area, suburban area, and outlying area, the outlying area has the highest comprehensive supply level score. Before 2017, the supply level in the suburban area was higher than that in the core area. However, after 2017, the supply level in the core area began to surpass that in the suburban area and has been steadily increasing, while the supply level in the suburban area has gradually declined. Refer to Fig. 1 for details.Fig. 1 The supply levels of Beijing's three areas from 2014 to 2022

Spatial differences in supply levels

According to the decomposition method of the Dagum Gini coefficient, we calculated the Gini coefficients of the overall area, within the three areas (core area, suburban area, outlying area), among the three areas (core area—suburban area; core area—outlying area; suburban area—outlying area), as well as the transvariation intensity, along with the contribution rates of each part. The results of the measurements are shown in Table 3. Table 3 The decomposition results of Dagum Gini coefficient

Year	Total Gini coefficient	Intra-areal Gini coefficient	Inter-areal Gini coefficient	Transvariation intensity	Contribution rate (%)	
Core area	Suburban area	Outlying area	Core area—Suburban area	Core area—Outlying area	Suburban area—Outlying area	Intra-areal	Inter-areal	Transvariation intensity	
2014	0.34190	0.44458	0.29214	0.079092	0.40263	0.41241	0.31752	0.067843	25.6087	54.5486	19.8427	
2015	0.33206	0.42129	0.29958	0.071861	0.38398	0.40823	0.30645	0.059129	25.717	56.476	17.807	
2016	0.33588	0.39223	0.31126	0.086486	0.37579	0.39360	0.34202	0.069133	25.4067	54.011	20.5823	
2017	0.33730	0.39841	0.29501	0.090289	0.38072	0.36512	0.36921	0.082298	25.459	50.1421	24.3989	
2018	0.31889	0.40006	0.29779	0.022658	0.38786	0.32158	0.33983	0.083369	26.0739	47.7822	26.1439	
2019	0.30885	0.35082	0.30111	0.039852	0.37414	0.29135	0.35149	0.070852	26.3177	50.7417	22.9406	
2020	0.29736	0.32628	0.28883	0.037843	0.35982	0.26696	0.36001	0.059956	26.0510	53.7866	20.1624	
2021	0.29768	0.33351	0.27353	0.046465	0.34978	0.27634	0.36484	0.058024	25.7013	54.8065	19.4922	
2022	0.32100	0.29200	0.34854	0.048760	0.38609	0.28675	0.40946	0.056892	24.8949	57.3815	17.7236	

Overall differences

The total Gini coefficient of the supply level of professional public health resources in Beijing decreased from 0.34190 in 2014 to 0.32100 in 2022, showing a downward trend overall, indicating that the areal differences in the supply level of professional public health resources in Beijing are gradually narrowing. In terms of specific evolutionary processes, the Gini coefficient of the supply level of professional public health resources in Beijing showed the most significant downward trend from 2017 to 2020, indicating a significant reduction in differences, but from 2021 to 2022, there was a significant upward trend. Refer to Fig. 2.Fig. 2 The evolution of the total Dagum Gini coefficient of supply levels in Beijing from 2014 to 2022

Intra-areal difference

From the perspective of areal differences, the evolution of difference in the three areas follows the pattern of "core area > suburban area > outlying area," as shown in Fig. 3. The Gini coefficient in core area is at a relatively high level, indicating the greatest disparity in the supply levels of professional public health resources among different districts within core area, such as the significant differences between Dongcheng, Xicheng, and Chaoyang, Fengtai. until 2021, the Gini coefficient in the suburban area was lower than that in core area, but it increased in 2022, so higher disparities in supply levels compared to core area. The supply level disparities in the outlying area have consistently remained at a relatively low level. In terms of the evolving trends, from 2014 to 2022, the Gini coefficient in core area showed the most significant downward trend. The outlying area experienced a notable decrease in 2017–2018, with relatively stable changes in other years. The suburban area showed a small decrease over the nine-year period, with a significant increase in 2021–2022.Fig. 3 The evolution of the intra-areal Dagum Gini coefficient of supply levels in Beijing from 2014 to 2022

Inter-areal difference

In terms of the size of differences, the range of differences among the three areas is between 0.266 and 0.412, with no significant fluctuations observed during the observation period. In terms of the evolution process, the difference between core area and outlying area was greatest in 2014, and gradually decreased thereafter. On the other hand, the inter-areal difference between suburban area and outlying area, changed from the minimum value in 2014 to the maximum value in 2022, showing a continuous increasing trend, this indicates a significant shift in differences among areas over the nine-year period. The Gini coefficient between core area and outlying area remained relatively stable, with minor changes during the observation period. See Fig. 4 for details.Fig. 4 The evolution of the inter-areal Dagum Gini coefficient of supply levels in Beijing from 2014 to 2022

Source and contribution rate of differences

From the perspective of trend changes, the contribution rate of intra-areal differences remains stable, while the contribution rate of inter-areal differences fluctuates significantly, and has been continuously increasing from 2018 to 2022. The contribution rate of inter-areal differences ranges from 47.78% to 57.38%, with an average contribution rate of 53.30%; the contribution rate of intra-areal differences ranges from 24.90% to 26.32%, with an average contribution rate of 25.69%. The contribution rate of the transvariation intensity is the smallest, indicating that the supply level of professional public health resources in Beijing shows a low degree of overlap among the three areas. In summary, inter-areal differences are the main cause of the disparity in the supply level of professional public health resources in Beijing. Therefore, narrowing the differences between areas and preventing the expansion of differences within areas will be the key focus for promoting the balanced development of the professional public health resource supply level in Beijing in the future. As shown in Fig. 5.Fig. 5 Sources and contribution rates of supply level differences in Beijing from 2014 to 2022

The dynamic evolution process of supply level

According to the results of KDE, Beijing's professional public health resource supply level shows the following characteristics in its dynamic evolution process: (1) Distribution position: the center position of the Kernel density curve moved overall to the right from 2014 to 2019, starting at 0.4. It slightly shifted to the left from 2020 to 2022, indicating an upward trend in supply level from 2014 to 2019, reaching its peak in 2020 and gradually decreasing from 2021. (2) Distribution form: the peak values of the Kernel density curve in 2015 and 2020 are relatively large. From 2016 to 2020, the peak values gradually increased, and the peak width decreased, indicating a concentration trend in the distribution of Beijing's professional public health resources, with a decrease in absolute differences. However, from 2021 to 2022, the peak values decreased, and the peak width increased, indicating an increased disparity in the supply level, consistent with the results of the Dagum Gini coefficient in the previous Sect. (3) The number of peaks: In 2014 and 2015, there were small side peaks, possibly because in 2015, China proposed to optimize resources and actively promote the integration of maternal and child health and family planning technical service institutions and responsibilities [53], which led to a sharp decrease in the number of family planning technical service institutions and some fluctuations in the supply level of professional public health resources. In other years, there was a single peak distribution, indicating that the overall supply level of professional public health resources in Beijing did not form a situation of two or multi-level differentiation. As shown in Fig. 6.

Fig. 6 The Kernel density curve of the supply level in Beijing from 2014 to 2022

Discussion

In this study, the mean comprehensive score of the supply level in outlying area is higher than that in the core and suburban areas. The reasons for this might be as follows: although the core area has an absolute quantity advantage in sanitary resources compared to other areas [11], the registered population and the floating population are much larger than those in the suburban and outlying areas, leading to a greater demand for public health resources. In economically developed core areas such as Haidian and Chaoyang District [54], a large number of floating populations and registered populations share public health resources matched only according to the registered population, which evidently significantly compresses the per capita supply resources.

The results of the Dagum Gini coefficient indicate that the differences in the level of supply of professional public health resources in Beijing have gradually decreased before 2021 but increased after 2021. Meanwhile, the Kernel density curve shows that the supply level was on the rise before 2021, peaked in 2021, and gradually decreased thereafter. The reasons may be as follows: before 2021, all districts of Beijing continued to consolidate the achievements of medical reform, actively explored the path of homogenization and high-quality supply of health resources, overall improving the level of supply in Beijing and reducing the differences between areas. Additionally, since the outbreak of the COVID-19 pandemic, the political center status and population size of the capital have amplified the risks in public health and healthcare [55]. The Beijing municipal government has swiftly directed various departments to respond, established emergency management systems, and implemented a series of precise control measures to effectively contain the spread of the epidemic [56]. During the epidemic prevention and control process, by increasing the number of health technical personnel, protective equipment, and other health resources, the risk of the epidemic has been significantly reduced, stabilizing the level of supply of public health resources. After 2021, with the preparation and hosting of the 2022 Winter Olympics and Paralympics in Beijing, on the basis of a large local population, the complex structure of foreign personnel and strong mobility [57], leading to a multiple-fold increase in demand for medical resources in a short period of time. Additionally, the population mobility and response policies in various districts vary, becoming an important reason for the reduction in the supply level of professional public health resources in different areas of Beijing and the increase in areal disparities.

From the perspective of intra-areal differences, the Gini coefficient in the core area is the largest, indicating the greatest disparity in supply levels. Combining the ranking of supply levels, it can be observed that Dongcheng and Xicheng are among the top five, while districts like Haidian and Chaoyang are at a moderate to lower level. The reasons for this disparity may be as follows: Firstly, Dongcheng and Xicheng serve as the central districts for politics, culture, and international exchanges [58], and their special status and functions increase the difficulty and complexity of public health management. Therefore, the government invests more resources to provide support and security. Secondly, districts like Haidian and Chaoyang, though economically prosperous, may have more resources allocated to fields such as technology, education, and finance, resulting in relatively fewer opportunities for investment in public health resources. In conclusion, the development of supply levels in various districts within the core area varies, leading to significant disparities in supply levels in the core area.

In terms of inter-areal differences, the difference between core and outlying areas is gradually narrowing, while the difference between suburban and outlying areas is gradually increasing. The reasons may be as follows: Firstly, the outlying area of Beijing serve as ecological conservation zones, with certain limitations on the development of healthcare and industry. However, with the convening of the 18th National Congress of the Communist Party of China, Beijing has explored the path of urban–rural integration with the concept of " The city drives the suburbs,and suburbs serve the city," implementing and promoting various suburbanization policies, thereby enhancing the overall development level of distant suburban area [59]. Additionally, the population size of outlying area is relatively small compared to other areas, which has increased the per capita availability of public health resources and reduced the areal differences with core areas. Secondly, according to the 7th National Population Census data of Beijing, in the past decade, the newly added permanent population in suburban area has exceeded 2 million, making it an important area for population decongestion from the central urban area of Beijing [60]. The increase in population size in suburban area has led to a higher demand for public health services, to some extent reducing their own professional public health resource supply level and enlarging the areal disparities between suburban and outlying area.

From the contribution rate, it can be seen that the impact of inter-areal differences on the overall disparity in the supply level of professional public health resources in Beijing is the greatest. Due to variations in economic development, social factors, supporting policies, etc., across different areas, the areal disparities in the supply of professional public health resources are objectively present and are difficult to completely change in a short period of time; these inter-areal differences may persist in the long term. Therefore, it is necessary to consider the demand for health services and, in the distribution process, take corresponding measures to tilt towards relatively weaker districts, narrowing the areal disparities to a reasonable range and achieving the optimal allocation [61].

Conclusion

The overall supply level of professional public health resources in Beijing is on the rise, with no clear trend of two-tier or multi-tier differentiation. However, there are still differences in the supply levels in Beijing, mainly stemming from areal disparities. The phenomenon of increased disparities after 2021 cannot be ignored. The issue of unbalanced and insufficient development persists. Therefore, this study puts forward the following recommendations.

First, optimize the allocation of public health resources and increase financial investment. In order to ensure that the quantity of public health human resources and health physical resources is sufficient and vibrant, in addition to health personnel possessing basic professional qualifications and health institutions having advanced medical equipment, government financial investment plays a crucial role. As decision-makers for public health resource allocation and investors in residents' health, not only Beijing but also governments in other provinces of China and other countries should increase financial investment and improve funding mechanisms. Enhancing the supply of professional public health resources is an effective way to alleviate areal disparities. On one hand, increasing the number of public health institutions can enhance the coverage of basic health services and the accessibility of the population. More service points can better meet the health needs of residents in the corresponding areas. On the other hand, increasing the number of health technicians can improve the quality and efficiency of professional public health services. Adequate health technicians can enhance the efficiency and cohesion of the entire institution when providing health support and implementing medical procedures. Finally, increasing the number of beds in professional public health institutions can reduce the overcrowding of medical institutions, improve patients' treatment experiences, and admit more residents in need of medical assistance, such as women and children. The core of reducing areal disparities lies in promoting equal opportunities. In conclusion, increasing the level of investment in professional public health institutions can significantly enhance the possibility for residents in various areas to access high-quality public health services, thereby promoting equal opportunities for fair access to health resources. Therefore, for areas with high population density but relatively limited professional public health resources, there should be increased investment in public health resources, with optimization and adjustment of institutions, beds, and health technical personnel resources. To address the significant disparities in the supply of resources between areas, the government should focus on achieving balanced distribution of public professional public health resources among various districts, establish and improve mechanisms for public health service guarantees for floating populations, address the issue of insufficient medical resources shared by the floating population and the registered residence population, and promote collaborative development among different areas.

Second, strengthen cooperation and communication between areas. One, strengthen the coordination and communication among the governments of various districts to ensure the consistency and coordination of health policies in each district, avoiding insufficient resource allocation or utilization rate decline; two, accelerate the establishment of a cooperation mechanism among professional public health institutions in various districts of Beijing, including holding regular meetings, formulating cooperation plans, sharing experiences and resources, etc., to promote cross-district health projects. These projects can include disease prevention and control, health education, medical resource sharing, etc., in order to provide support and assistance to districts lacking resources. Three, establish a talent exchange mechanism to encourage professional personnel from public health institutions in various districts to exchange and learn from each other, enhance the professional level of health workers, and jointly improve the level and quality of medical services. More importantly, Beijing should also strengthen its ties with other provinces, complement each other, and promote the sharing of high-quality resources.

Third, enhance publicity and education. The World Health Organization has identified numerous World Health Days related to health, setting corresponding promotional themes each year. It emphasizes that global public health awareness campaigns have enormous potential to enhance awareness and understanding of health issues. Therefore, increasing the publicity of public health and conducting health education activities for the general public is very meaningful. Conducting health education activities targeted at the general public is highly meaningful. On the one hand, it can help raise public awareness of the importance of public health resources. People learn how to use public health resources reasonably to ensure that resources can benefit a larger population to the fullest extent. The increasing demand from the public for health resources can also prompt the government to increase investment, improve public health facilities and services. On the other hand, disseminating public health knowledge to the masses, guiding citizens to develop good hygiene and health habits, helping people understand how to prevent diseases and other health issues, control the occurrence or spread of diseases, thereby reducing the pressure on the public health system, promoting the healthy development of society, and public welfare.

Supplementary Information

Supplementary Material 1.

Abbreviation

KDE Kernel density estimation

Acknowledgements

We appreciate the Beijing Municipal Health Big Data and Policy Research Center for its yearbooks, which provide the data for this study.

Authors’ contributions

RW, DG, YL, and GZ conceived the concept and design of the study. RW contributed to the analysis and interpretation of data, as well as the writing of the main manuscript text. DG and GZ completed the data collection and literature review. YL made substantial contributions to review and revise the manuscript. All authors have read and approved the final version.

Funding

Supports for this research were provided by the National Social Science Foundation of China (No. 22BGL247). The project is titled "Research on the Public Health Emergency Management System and Strategies of Major Cities at Home and Abroad from the Perspective of Normalization of Epidemic Prevention and Control."

Availability of data and materials

Sequence data that support the findings of this study have been deposited in the Beijing Municipal Health Big Data and Policy Research Center with the primary accession website http://www.phic.org.cn/tjsj/wstjjb/.

Declarations

Ethics approval and consent to participate

Not applicable. This study only analyzed data from published secondary sources and did not involve any specific human subjects.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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