Multi-scale inequalities in accessibility of hierarchical medical facilities in Chongqing, China: a comprehensive assessment of physician and bed resources

Multi-scale inequalities in accessibility of hierarchical medical facilities in Chongqing, China: a comprehensive assessment of physician and bed resources | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-scale inequalities in accessibility of hierarchical medical facilities in Chongqing, China: a comprehensive assessment of physician and bed resources Chao Tan, Wenliang Zhang, Ran Zheng, Wei Zhang, Caizhi Tang, Yu Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7421555/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The accessibility of medical service is directly related to the quality of residents' lives and has attracted increasing concerns of both researchers and policymakers. However, previous studies have paid few attentions to the multi-scale inequalities in the accessibility of hierarchical medical facilities in megacities, and lacks comprehensive assessment of both physician and bed resources. Using Chongqing, China as the study area, this study applied the Gaussian-based two-step floating catchment area (G2SFCA) method to measure the accessibility of hierarchical medical facilities considering both physician and bed resources. The Dagum Gini coefficient was employed to decompose the inequality in medical accessibility across multiple scales (the three major divisions and urban-rural divisions). Results show that the average accessibility of tertiary hospitals is the highest and its distribution is the most equal, whereas the accessibility of primary hospitals has the lowest average value and the highest inequality. The bed-based and physician-based accessibility exhibit obvious differences. The Pearson correlation coefficients between two types of accessibility are 0.982, 0.913, and 0.62 for tertiary, secondary and primary hospitals, respectively. From the perspective of multi-scale inequalities, whether the urban-rural division or the three major regional partitions, the value of intra-group inequality exceeded the inter-group inequality across all hospital levels, which indicated that intra-group inequality are the main sources of overall disparities. This study can shed new lights on the compositions of the inequality in hierarchical medical accessibility, and highlights the necessity of comprehensively considering physician and bed resources in medical accessibility assessment. Medical accessibility Multi-scale inequality Hospital beds Physicians Urban-rural disparity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The fairness and accessibility of medical services have become one of the core objectives in the construction of the national medical and health system under the guidance of “Healthy China 2030 Planning Outline” [ 1 ]. In particular, since the proposal of the hierarchical medical system in 2015, the accessibility of medical facilities at different levels has attracted significant attention from both academia and policymakers [ 2 , 3 ]. However, existing research has significant limitations. First, existing studies have mostly focused on facilities at a single scale or a single level [ 4 ], with insufficient cross-analysis between "hierarchical facilities" (tertiary hospital, secondary hospital, and primary hospital) and "multi-scale inequalities" (three major divisions and urban-rural divisions). Second, in terms of medical resource factors, most research centers on either the number of beds or the number of physicians, with insufficient consideration of the impact of both the number of beds and physicians on the accessibility of hierarchical medical facilities [ 5 ]. The acceleration of China's urbanization process and the rise of megacities have made the spatial allocation of medical resources increasingly complex. Data from the National Bureau of Statistics shows that, China had 11 megacities defined by a permanent population of over 10 million as of 2023. [ 6 ]. Among them, megacities such as Chongqing, Beijing, and Shanghai exhibit a composite spatial structure of large cities and vast rural areas, forming complex coupling relationships among urban-rural population mobility [ 7 , 8 ], regional economic disparities, and medical resource allocation. Existing studies on medical accessibility in Chinese cities, such as analyses of Beijing and Shanghai, have revealed the spatial inequality pattern of medical resource agglomeration in central urban areas and relative scarcity in suburban areas [ 9 – 11 ]. However, the research boundaries are mostly limited to urban areas, failing to deeply analyze the structural inequality of multi-scale medical service systems within urban regions and their impacts on accessibility. From an international perspective, studies on the Tokyo Metropolitan Area and Greater London have focused on the spatial allocation of medical resources in metropolitan areas [ 12 , 13 ], but they lack targeted discussions on urban regions in developing countries with prominent urban-rural binary structures. Since spatial analysis techniques were introduced into the field of medical geography in the 1970s, extensive research in China and abroad has explored the spatial distribution and accessibility of medical facilities. Early studies predominantly analyzed the distribution characteristics of medical resources based on static supply-demand ratios and distance-decay models [ 14 , 15 ]. With the innovation of GIS technology and spatial analysis methods, accessibility metrics based on time costs, such as the two-step floating catchment area (2SFCA) method and network analysis models, have gradually become mainstream [ 1 , 16 ]. As an improved spatial analysis method, the Gaussian two-step floating catchment area (G2SFCA) approach possesses unique advantages in researching medical resource accessibility [ 17 ]. Building on the traditional 2SFCA method, this approach simulates the distance-decay of medical service attractiveness by introducing a Gaussian function. Compared with the traditional 2SFCA, it more realistically reflects the spatial scope of medical service influence in reality and more accurately assesses the accessibility levels of medical resources at different spatial locations [ 18 ]. However, existing studies often treat medical resources as homogeneous elements, rarely distinguishing the service capability inequality among medical institutions of different levels, such as tertiary hospitals, secondary hospitals, and primary hospitals, as well as the heterogeneous roles of bed and physician resources in service provision. As the sole municipality directly under the Central Government in China’s central and western regions, Chongqing integrates a large urban agglomeration with vast rural areas, featuring a prominent urban-rural binary structure and evident spatial heterogeneity in medical resource allocation. Taking Chongqing as a typical case, this study focuses on the multi-scale inequalities in the accessibility of hierarchical medical facilities within Chinese megacity regions, aiming to address the following issues. First, breaking through the limitation that existing studies mostly focus on facilities at a single scale or a single level, this study, based on the accessibility of hierarchical medical facilities (tertiary hospital, secondary hospital, and primary hospital), examines the multi-scale inequalities (three major divisions and urban-rural divisions). Second, on the basis of considering both physicians and beds, through in-depth analysis of spatial inequality and urban-rural inequality, it systematically interprets the characteristics of medical accessibility in different urban divisions and urban-rural areas. Selecting Chongqing as the research object can not only provide decision-making support for optimizing medical resource accessibility in Chongqing but also offer practical insights for other megacities with similar urban-rural spatial structures. 2. Materials and Methods 2.1 Study area Chongqing is located in southwest China and has jurisdiction over 38 districts and counties, such as Shapingba, Yubei, and Youyang (Fig. 1 ). Chongqing is a typical mountainous city, with a total area of 82,400 km 2 , and 76% of the total area is mountainous (8). According to the 2024 Statistical Yearbook, Chongqing has a permanent population of 31,914,300. According to the “Chongqing Territorial Space Master Plan (2021–2035)”, Chongqing is divided into the city proper of Chongqing, the three gorges reservoir area in northeast Chongqing, and the Wuling mountain area in southeast Chongqing. The city proper of Chongqing is located in the central and western part of Chongqing, which has relatively flat terrain, developed economy and high level of urbanization. On the contrary, the three gorges reservoir area in northeast Chongqing and the Wuling mountain area in southeast Chongqing are dominated by large areas of mountainous land, with relatively low urbanization level and mainly rural population. At the same time, this study defined streets as urban areas and townships as rural areas to investigate inequalities in access to healthcare between urban and rural areas. 2.2 Data and pre-processing 2.2.1 Medical facility data The medical facility data was sourced from the Chongqing Health Commission. It mainly included hospital names, economic types, ratings, addresses, the number of practicing (assistant) physicians, and the number of actual beds. This study mainly considered 227 public hospitals in Chongqing (Fig. 2 ). These hospitals are classified into tertiary hospital, secondary hospital, primary hospital, and unrated hospital according to the hospital grades. Considering that the functions and other aspects of primary and unrated hospitals are relatively similar, primary and unrated hospitals are combined into primary hospitals. Among the 227 public hospitals, there are 89 tertiary hospitals, 80 secondary hospitals, and 57 primary hospitals. In total, there are 43,628 physicians and 135,468 hospital beds. The hospital address data were transformed to geographic spatial data based on the Amap geocoding API [ 19 ], and the calculations were executed by coding in Python 3.7 (manufacturer, city, state abbreviation if USA, country Guido van Rossum, Netherlands). 2.2.2 Permanent resident population data The data of the permanent resident population was sourced from the data of the Seventh National Population Census conducted by the Chongqing Municipal Bureau of Statistics [ 20 ], and has been adjusted using the data of each district and county in the Chongqing Statistical Yearbook 2024. The 100-m gridded population dataset of China’s seventh census was sourced from the data shared by Professor Chen Yuehong's team on the figshare platform [ 21 ]. The spatial scope of this dataset covers the whole country, and the data year is 2020. The spatial resolution is 100 meters, and the data coordinate system is Albers_Conic_Equal_Area. The data format is raster data, and the raster value represents the population number of each grid (with an approximate area of 100m*100m). This dataset was used to calculate the population-weighted centroids as the population center of streets or towns for the calculation of accessibility [ 22 ]. 2.2.3 Real-time travel time data Real-time travel time were obtained from the Amap route optimization service API v2.0 [ 23 ], which based on comprehensive road network information and combines real-time traffic conditions to provide users with accurate route optimization capabilities across multiple terminals. The navigation service of Amap provides route planning, mileage, and travel time. And its travel modes include driving route optimization, public transportation route optimization, cycling route optimization, and walking route optimization. Driving route optimization was used to calculate the travel time from each population-weighted centroid to a center level of hospital in this paper. To circumvent peak traffic periods, holidays, and other exceptional circumstances, the calculation was carried out between 9:00 and 12:00 a.m. on weekdays from April 14 to 29, 2025 [ 24 ]. 2.3 Methods 2.3.1 Calculate the weighted population centroid The population-weighted centroid refers to the centroid position of a region when the population distribution is taken into account. In practical research, calculating accessibility using the geometric centers of streets often overlooks the heterogeneity of population distribution, leading to deviations in the calculation results [ 22 ]. Therefore, it is more accurate to calculate spatial accessibility using the weighted population centroid. The 100-m gridded population dataset of China’s seventh census was used to calculate the population-weighted centroids as the population center of streets or towns for the calculation of accessibility. The formula for calculating the weighted population centroid is as follows: $$\:\overline{X}=\:\frac{{\sum\:}_{i=1}^{n}{P}_{i}{X}_{i}}{{\sum\:}_{i=1}^{n}{P}_{i}}$$ 1 $$\:\overline{Y}=\:\frac{{\sum\:}_{i=1}^{n}{P}_{i}{Y}_{i}}{{\sum\:}_{i=1}^{n}{P}_{i}}$$ 2 Where \(\:\overline{X}\) and \(\:\overline{Y}\) are respectively the abscissa and ordinate of the population-weighted centroid. \(\:{X}_{i}\:\) and \(\:{Y}_{i}\) respectively represent the geographical coordinates of the i area. n represents the number of streets or townships in Chongqing. \(\:{P}_{i}\) represents the population quantity of the i street. The population-weighted centroids were calculated by using ArcGIS Pro (Esri, Redlands, CA, USA), and its coordinates were converted to GCJ-02 using Python 3.7, aiming to unify the coordinate systems for calculating travel time. 2.3.2 Calculate the spatial accessibility G2SFCA is an improved accessibility model based on the traditional 2SFCA method by introducing Gaussian kernel functions. Its core lies in the 2SFCA method, first determining the spatial scope of the facility, and then using the Gaussian equation to empower within this range, and finally calculating the accessibility index of the facility‌. The steps are as follows. In the first step, all demand nodes within the catchment area of each facility are searched for, and then the supply-demand ratio for each facility is calculated [1]. The formula can be written as: $$\:{R}_{j}=\frac{{S}_{j}}{\sum\:_{k=1}^{m}{D}_{k}\times\:f\left({d}_{ij}\right)}$$ 3 $$\:f\left({d}_{ij}\right)=\left\{\begin{array}{c}\frac{{e}^{-1/2\times\:({{d}_{ij}/{d}_{0})}^{2}-}{e}^{-1/2}\:\:}{1-{e}^{-1/2}\:\:},\:\:\:\\\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:0,\:\:\:\:\:\:\:{\:\:d}_{ij}>{d}_{0}\end{array}\right.{d}_{ij}\le\:{d}_{0}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4\right)$$ Where \(\:{R}_{j}\) is supply to demand ratio, \(\:{S}_{j}\) is the capacity of supply at location j , the number of hospital beds and physicians are used to represent it. \(\:{D}_{k}\) is the demand amount, which is represented by the population quantity in the region. \(\:{d}_{ij}\:\) is the travel time between j and i , \(\:f\left({d}_{ij}\right)\) is distance decay function, \(\:{d}_{0}\) is the size of catchment area. In this study, different search radii \(\:{d}_{0}\) are established according to the varying grades of hospitals. Specifically, the service radius of hospitals that of primary hospitals is set at 60 minutes, that of secondary hospitals is set at 90 minutes, and that of tertiary hospitals is set at 150 minutes [ 25 ]. In the second step, the supply-demand ratios of all facilities within the catchment area of each demand node are summed up. The sum of the supply-demand ratios for each demand node serves as its spatial accessibility score. The formula can be written as: $$\:{A}_{i}=\sum\:_{j=1}^{n}{R}_{j}\times\:f\left({d}_{ij}\right)$$ 5 Where \(\:{A}_{i}\) is the accessibility of medical services at demand node i . 2.3.3 Calculating the equitability of healthcare allocation The Dagum Gini coefficient, an extended model of the Gini coefficient, is a statistical method proposed by Italian economist Carmelo Dagum for analyzing inequality in income or resource allocation[ 26 , 27 ]. Compared with the traditional Gini coefficient, the Dagum Gini coefficient can not only measure the overall inequality level but also decompose inequality into intra-group inequality, inter-group inequality, and hypervariable density. The Dagum Gini coefficient is used to measure the equality of resource allocation. The closer it is to 0, the fairer the allocation; the closer it is to 1, the more unfair the allocation. Intra-group inequality reflects the resource disparities among hospitals within the same division; Inter-group inequality embodies the resource inequality between different divisions; Hyper-variable density contribution demonstrates the complex overlapping patterns of resource distribution; and the respective contribution percentages indicate the degree to which each component influences the overall Gini coefficient. The specific formula is: $$\:G=\frac{{\sum\:}_{j=1}^{k}{\sum\:}_{i=1}^{{n}_{j}}{\sum\:}_{r=1}^{{n}_{h}}\left|{x}_{ji}-{x}_{hr}\right|}{2{n}^{2}\stackrel{-}{y}}$$ 6 Where, G is the overall Gini coefficient, k is the number of streets or townships in Chongqing, \(\:{n}_{j}\) and \(\:{n}_{h}\) respectively represent the sample sizes of different division j and h, \(\:{x}_{ji}\) and \(\:{x}_{hr}\) respectively represent the individual values in different division j and h , and \(\:\stackrel{-}{y}\) is the sample mean in the division. The Dagum Gini coefficient can be further decomposed into three components. The formula is: $$\:G={G}_{w}+{G}_{nb}+{G}_{t}$$ 7 Where \(\:{G}_{w}\) is the contribution of intra-group inequality. \(\:{G}_{nb}\) is the contribution of inter-group inequality. \(\:{G}_{t}\) is the intensity of cross-region variation. This study was based on the accessibility of public hospitals in Chongqing. It calculated the Dagum Gini coefficients and their decompositions under different hospital levels (tertiary, secondary, primary), division types (three major divisions, urban - rural divisions), and per capita medical resources (physicians, beds). 3. Results 3.1. Spatial accessibility of tertiary hospitals and its inequality 3.1.1 Spatial accessibility of tertiary hospitals The spatial accessibility of tertiary hospitals based on the number of hospital beds were calculated with a service radius defined by a 150-minute travel time threshold (Fig. 3 A). The range of spatial accessibility for per thousand persons in Chongqing is 0-4.552, which indicates that the accessibility of medical resources in Chongqing based on bed capacity exhibits obvious spatial inequality. From the perspective of the regional division, the city proper of Chongqing ranks the highest has the highest value (3.078), followed by the Wuling mountain area in southeast Chongqing (2.16), and the three gorges reservoir area in northeast Chongqing has the lowest value (1.916). When looking at each district and county in Chongqing, the top three districts or counties are Jiangbei (4.243), Nanan (4.176), Dadukou (3.995), respectively, and the three districts or counties with the lowest average accessibility are Chengkou (0. 512), Wushan (1.277), Fengjie (1.357), respectively. When analyzed at the sub-district and township scale, overall, the accessibility decreases gradually from the central city to the surrounding areas. The top three areas with the highest accessibility of medical resources are Sanhe (4.552) and Daxie (4.409) in Shizhu, Heixi (4.401) in Qiangjian, respectively, while the three areas with the lowest overall accessibility (the value is 0) are Houping and Gaonan in Chengkou, Peishi, Duping and Dengjia in Wushan, respectively. The spatial accessibility of tertiary hospitals based on physicians were calculated with a service radius defined by a 150-minute travel time threshold (Fig. 3 B). The spatial accessibility for per thousand persons of medical resources in Chongqing spans from 0 to 1.577. This further demonstrates, in the context of physician-based availability, that significant variations exist in the accessibility of medical resources across the city. From the perspective of the regional divisions, the accessibility of medical resources based on physician exhibits the similar characteristics to that based on bed capacity. the city proper of Chongqing ranks the highest has the highest value (1.117), followed by the Wuling mountain area in southeast Chongqing (0.782), and the three gorges reservoir area in northeast Chongqing has the lowest value (0.569). Among the districts or counties of Chongqing, Jiangbei (1.532), Nanan (1.516), and Dadukou (1.466) take the top three spots, and Chengkou (0.136), Wushan (0.346), and Fengjie (0.386) are the three districts and counties with the lowest average accessibility. When analyzed at the sub-district and township scale, the top three areas with the highest accessibility of medical resources are Guojiatuo (1.576) in Jiangbei, Heixi (1.574) in Qiangjian, Yuzui (1.563) in Jiangbei, respectively, while the districts or counties with low values of accessibility both based on physician and bed capacity are the same, and the values of accessibility in these districts or counties are all zero. The Pearson correlation coefficient was calculated using bed-based accessibility and physician-based accessibility of tertiary hospitals at different scales. From the perspective of counties and districts, The Pearson correlation coefficient was 0.987 and a P-value below 0.01. When analyzed at the sub-district and township scale, the Pearson correlation coefficient was 0.982 and a P value below 0.01. This indicates a high degree of correlation between the accessibility of tertiary hospitals calculated based on bed resources and that based on physician resources. 3.1.2 Multi-scale inequality in accessibility of tertiary hospitals The Dagum Gini coefficient of accessibility for tertiary hospitals was calculated under different medical resources (beds, physicians) and different regional divisions (three major divisions, urban-rural divisions) (Table 1 ). Across both the three major divisions and the urban-rural divisions, the overall Gini coefficient for bed-based accessibility was 0.2553, which was lower than that for physician-based accessibility (0.2756). This suggested a more uneven distribution of physician-based accessibility among tertiary hospitals. From the perspective of the three major divisions, the intra-group inequality value of bed-based accessibility was 0.1418, the inter-group inequality value was 0.1135, and the hyper-variable density value was 0, accounting for 55.5%, 44.5%, and 0% respectively. Meanwhile, the intra-group inequality value based on physician accessibility was 0.1442, the inter-group inequality value was 0.1314, and the super-variation density value also was 0, with their respective proportions being 52.3%, 47.7%, amd 0%. From the perspective of the urban-rural divisions, the intra-group inequality value of bed-based accessibility was 0.1771, the inter-group inequality value was 0.0782, and the hyper-variable density value was 0, with their respective proportions amounting to 69.4%, 30.6%, and 0%. Meanwhile, the intra-group inequality value based on physician accessibility was 0.1903, the inter-group inequality value was 0.0853, and the super-variation density value also was 0, with their respective proportions being 69%, 31%, and 0%. Table 1 The Dagum Gini coefficients and their decompositions of tertiary hospitals under different dimensions Division type Resource type Overall Gini coefficient Intra- group inequality Inter- group inequality Hyper- variable density Intra- group contribution (%) Inter- group contribution (%) Hyper- variable density contribution (%) Three-major Bed 0.2553 0.1418 0.1135 0 55.5 44.5 0 Three-major Physician 0.2756 0.1442 0.1314 0 52.3 47.7 0 Urban-rural Bed 0.2553 0.1771 0.0782 0 69.4 30.6 0 Urban-rural Physician 0.2756 0.1903 0.0853 0 69 31 0 3.2. Spatial accessibility and its inequality of secondary hospitals 3.2.1 Spatial accessibility of secondary hospitals Utilizing a 90-minute travel time criterion to define the service radius, the spatial accessibility of secondary hospitals determined by the number of hospital beds was calculated (Fig. 4 A). Its accessibility range is 0-2.869, which is far lower than the accessibility of tertiary hospitals in terms of bed capacity. From the perspective of regional divisions, the ranking of the three regions is consistent with that of tertiary hospitals. The city proper of Chongqing ranks the highest has the highest value (0.872), followed by the Wuling mountain area in southeast Chongqing (0.673), and the three gorges reservoir area in northeast Chongqing has the lowest value (0.64). When examining each district and county in Chongqing, Chengkou, boasting a value of 1.919, Dadukou with a value of 1.433, and Nanan with a value of 1.314 are respectively the top three districts or counties. Meanwhile, Yunyang, having a value of 0.244, Shizhu with a value of 0.34, and Tongnan with a value of 0.349 are respectively the three districts or counties with the lowest average accessibility. When conducting an analysis at the sub-district and township scale, the three areas that rank at the top are Gecheng (2.869), Fuxing (2.86) and Longtian (2.792) in Dazu. Moreover, the accessibility of 66 sub-districts or townships is 0, and they are distributed in 30 districts and counties such as Yubei, Wanzhou, Youyang, etc. The spatial accessibility of secondary hospitals based on the number of physicians is calculated with a service radius of 90 minutes (Fig. 4 B). The scope of accessibility is 0-0.74. From the perspective of regional division, the city proper of Chongqing ranks the highest (0.213), followed by the three gorges reservoir area in northeast Chongqing (0.166), and finally the Wuling mountain area in southeast Chongqing (0.157). In the various districts and counties of Chongqing, Chengkou (0.492), Dadukou (0.389), and Jiulongpo (0.349) occupy the top three positions, while Fengdu (0.056), Tongnan (0.06), and Shizhu (0.078) have the lowest average levels of medical resource accessibility. Upon analysis at the sub-district and township level, the top three rankings for both physician-based and bed-based medical resource accessibility are identical. Similarly, 66 sub-districts or townships exhibit zero accessibility. The Pearson correlation coefficient was calculated using bed-based accessibility and physician-based accessibility of secondary hospitals at different scales. From the perspective of counties and districts, The Pearson correlation coefficient was 0.888 and a P-value below 0.01. When analyzed at the sub-district and township scale, the Pearson correlation coefficient was 0.913 and a P-value equaled 0. This suggested a marginally lower degree of correlation between the accessibility of secondary hospitals calculated based on bed resources and that based on physician resources. 3.2.2 Multi-scale inequality in accessibility of secondary hospitals The Dagum Gini coefficient of accessibility for secondary hospitals was calculated under different medical resources (beds, physicians) and different regional divisions (three major divisions, urban-rural divisions) (Table 2 ). In both the three major divisions and the urban-rural divisions, the overall Gini coefficient for bed-based accessibility was 0.3744, which was lower than that for physician-based accessibility (0.3968). This slao suggested a more uneven distribution of physician-based accessibility among secondary hospitals. From the perspective of the three major divisions, the intra-group inequality value of bed-based accessibility was 0.2193, the inter-group inequality value was 0.1541, and the hyper-variable density value was 0.001, accounting for 58.6%, 41.2%, and 0.3% respectively. Meanwhile, the intra-group inequality value based on physician accessibility was 0.2352, the inter-group inequality value was 0.1606, and the super-variation density value also was 0.001, with their respective proportions being 59.3%, 40.5%, amd 0.3%. From the perspective of the urban-rural divisions, the intra-group inequality value of bed-based accessibility was 0.2645, the inter-group inequality value was 0.1099, and the hyper-variable density value was 0, with their respective proportions amounting to 70.7%, 29.3%, and 0%. Meanwhile, the intra-group inequality value based on physician accessibility was 0.2739, the inter-group inequality value was 0.1229, and the super-variation density value also was 0, with their respective proportions being 69%, 31%, amd 0%. Table 2 The Dagum Gini coefficients and their decompositions of secondary hospitals under different dimensions Division type Resource type Overall Gini coefficient Intra- group inequality Inter- group inequality Hyper- variable density Intra- group contribution (%) Inter- group contribution (%) Hyper- variable density contribution (%) Three-major Bed 0.3744 0.2193 0.1541 0.001 58.6 41.2 0.3 Three-major Physician 0.3968 0.2352 0.1606 0.001 59.3 40.5 0.3 Urban-rural Bed 0.3744 0.2645 0.1099 0 70.7 29.3 0 Urban-rural Physician 0.3968 0.2739 0.1229 0 69 31 0 3.3. Spatial accessibility and its inequality of primary hospitals 3.3.1 Spatial accessibility of primary hospitals With a 60-minute travel time used to demarcate the service radius, the spatial accessibility of primary hospitals gauged by the number of hospital beds available was calculated (Fig. 5 A). The range of accessibility is 0-1.578. The area with the highest accessibility is three gorges reservoir area in northeast Chongqing (0.127), and its distribution is obvious regional inequality. Next comes the the city proper of Chongqing (0.114), where there is relatively uniform. The accessibility in the Wuling mountain in southeast Chongqing region is very low (0.001). Among all the districts and counties in Chongqing, the accessibility of Jiangjin (0.328), Wuxi (0.318), and Yunyang (0.315) rank in the top three positions. In contrast, Youyang, Pengshui, Xiushan, Qiangjiang and Chengkou are the five districts and counties with the accessibility value of zero. When the accessibility of medical resources is examined at the sub-district and township level, Shuangjiang in Yunyang (1.578), Huangshi (1.432), and Renhe (1.391) emerge as the top three regions with the highest levels of medical resource accessibility. Additionally, 429 sub-districts and townships exhibit zero medical resource accessibility. These areas are scattered across 38 districts and counties. Employing a 60-minute service radius, the spatial accessibility of primary hospitals measured by the number of physicians is computed (Fig. 5 B). The accessibility range amounts to 0-0.081. From the perspective of regional division, the accessibility rankings and spatial distributions based on the number of physicians and based on the number of hospital beds exhibit a different characteristic. The city proper of Chongqing ranks the highest (0.021), followed by the three gorges reservoir area in northeast Chongqing (0.009), and finally the Wuling mountain area in southeast Chongqing (0). Among all Chongqing's districts and counties, Dadukou (0.045), Changshou (0.042) and Jiangbei (0.037) are the leading three, showing a relatively good status. However, Youyang, Pengshui, Xiushan, Qiangjiang and Chengkou are the five districts and counties with the accessibility value of zero. When analyzing medical resource accessibility at the sub-district and township level, Dandu in Changshou (0.081), Lidu (0.08) and Chognyi (0.078) in Fuling are the top three regions with the highest accessibility. The Pearson correlation coefficient was calculated using bed-based accessibility and physician-based accessibility of primary hospitals at different scales. From the perspective of counties and districts, the Pearson correlation coefficient was 0.488 and a P-value equaled 0.019. When analyzed at the sub-district and township scale, the Pearson correlation coefficient was 0.62 and a P-value below 0.01. This indicated very low degree of correlation between the accessibility of primary hospitals calculated based on bed resources and that based on physician resources. 3.3.2 Multi-scale inequality in accessibility of primary hospitals The Dagum Gini coefficient of accessibility for primary hospitals was calculated under different medical resources (beds, physicians) and different regional divisions (three major divisions, urban-rural divisions) (Table 3 ). Whether it was the three major divisions or the urban-rural divisions, the overall Gini coefficient of bed-based accessibility was 0.7622, which was higher than that of physician-based (0.6918), indicating that the distribution of bed-based accessibility in primary hospitals was more uneven. From the perspective of the three major divisions, the intra-group inequality value of bed-based accessibility was 0.3959, the inter-group inequality value was 0.2827, and the hyper-variable density value was 0.0835, accounting for 51.9%, 37.1%, and 11% respectively. Meanwhile, the intra-group inequality value based on physician accessibility was 0.346, the inter-group inequality value was 0.2826, and the super-variation density value was 0.0632, with their respective proportions being 50%, 40.8%, amd 9.1%. From the perspective of the urban-rural divisions, the intra-group inequality value of bed-based accessibility was 0.507, the inter-group inequality value was 0.2282, and the hyper-variable density value was 0.027, with their respective proportions amounting to 66.5%, 29.9%, and 3.5%. Meanwhile, the intra-group inequality value based on physician accessibility was 0.4153, the inter-group inequality value was 0.2475, and the super-variation density value was 0.029, with their respective proportions being 60%, 35.8%, amd 4.2%. Table 3 The Dagum Gini coefficients and their decompositions of primary hospitals under different dimensions Division type Resource type Overall Gini coefficient Intra- group inequality Inter- group inequality Hyper- variable density Intra- group contribution (%) Inter- group contribution (%) Hyper- variable density contribution (%) Three-major Bed 0.7622 0.3959 0.2827 0.0835 51.9 37.1 11 Three-major Physician 0.6918 0.346 0.2826 0.0632 50 40.8 9.1 Urban-rural Bed 0.7622 0.507 0.2282 0.027 66.5 29.9 3.5 Urban-rural Physician 0.6918 0.4153 0.2475 0.029 60 35.8 4.2 4. Discussions Taking Chongqing as a case study, this study investigates the spatial and urban-rural inequalities in the accessibility of hierarchical medical facilities in Chinese megacities. In general, by setting the service radius of tertiary hospitals at 150 minutes, that of secondary hospitals at 90 minutes, and that of primary hospitals at 60 minutes to study the accessibility, it is found that the average accessibility of tertiary hospitals in Chongqing is the highest (2.486), followed by secondary hospitals (0.751), and finally primary hospitals (0.097). Compared with other cities, although there are inequality in the setting of the service radius, the research results also show that the accessibility of hospitals with higher grades is higher [ 4 , 24 , 28 ]. The accessibility of a hospital is the result of the combined effects of its strategic positioning, resource aggregation, geographical concentration, transportation facilities, and patient preferences [ 9 , 29 ]. Hospitals of higher grades have higher accessibility due to their advantageous conditions. From the perspective of regional division, whether it is the accessibility based on hospital beds or that based on physicians, the accessibility of hospitals of different levels in the city proper of Chongqing is the highest. Previous studies, from the perspective of the equality of medical resource allocation, have found that there is a significant inequality in the allocation of medical resources in Chongqing [ 8 ]. There is a large quantity of high-quality medical resources in the city proper of Chongqing, which leads to the accessibility of medical resources in the main urban area being higher than that in other regions. This is consistent with the research findings of other cities [ 4 , 16 , 24 ]. There is a relatively small difference in the average accessibility between the three gorges reservoir area in northeast Chongqing and the Wuling mountain area in southeast Chongqing. This may have something to do with the number of hospitals, which is 162 in the city proper of Chongqing, much higher than in the three gorges reservoir area in northeast Chongqing (with 47 public hospitals) and in the wuling mountains in southeastern Chongqing (18 public hospitals). The accessibility of tertiary hospitals in the Wuling mountain area in southeast Chongqing is higher than that in the three gorges reservoir area in northeast Chongqing. The accessibility of secondary hospitals based on physicians in the three gorges reservoir area in northeast Chongqing is higher than that in the Wuling mountain area in southeast Chongqing, while the accessibility of secondary hospitals based on hospital beds in the three gorges reservoir area in northeast Chongqing is lower than that in the Wuling mountain area in southeast Chongqing. The accessibility of primary hospitals in the Wuling mountain area in southeast Chongqing is lower than that in the three gorges reservoir area in northeast Chongqing. Analyzed from the scale of districts and sub-districts, the districts or sub-districts with high values of medical resource accessibility are not all located in the urban area of Chongqing, which indicates that although economic factors are an important factor affecting the accessibility of medical resources, the accessibility of medical resources is affected by a combination of multiple factors [ 30 ]. The study found that accessibility based on bed numbers and physician counts exhibits similar trends, indicating a high degree of correlation in the spatial distribution of these two resource elements. Moreover, this correlation tends increased with the escalation of hospital hierarchy. This phenomenon was most pronounced in tertiary hospitals, with an Pearson correlation coefficient exceeding 0.982 and a P-value equaling 0. Studies on traditional Chinese medicine resources in China also show that the fluctuations in the Gini coefficients of beds and health personnel are synchronized [ 31 ]. This may stem from the linked mechanism of bed-staffing in China's hospital resource allocation, leading to synchronous agglomeration or sparsity of both between regions. The Dagum Gini coefficient was calculated for different levels of hospitals (primary, secondary, and tertiary) under different medical resources (beds, physicians) and different regional divisions (three major divisions, urban-rural divisions). From the overall Gini coefficient of different levels of hospitals, it was found that the overall Gini coefficient of primary hospitals was 0.6918–0.7622, that of secondary hospitals was 0.3744–0.3968, and that of tertiary hospitals was 0.2553–0.2756, indicating that the accessibility distribution of medical resources in primary hospitals was the most unbalanced, followed by secondary hospitals, and the accessibility distribution of tertiary hospitals was the most balanced. A possible reason is the insufficient number of primary hospitals, which is 57, while the numbers of secondary and tertiary hospitals are 80 and 89, respectively. Although tertiary hospital accessibility is relatively balanced, it should be noted that this relative balance may only be reflected in geographical spatial distribution, rather than equity in service utilization, for example, tertiary hospitals are concentrated in major cities, leading to limited actual utilization by rural/low-income groups [ 9 ]. From the perspective of resource types, the characteristics of the overall Gini coefficient based on bed accessibility and based on physician accessibility are different in different levels of hospitals. Among them, the overall Gini coefficient of physician-based accessibility in tertiary and secondary hospitals was slightly higher than that of bed-based accessibility, indicating that the physician-based accessibility distribution in tertiary and secondary hospitals was relatively more unequal than that in bed-based accessibility. In contrast, the overall Gini coefficient of bed-based accessibility was 0.7622 in primary hospitals than that of physician-based (0.6918), indicating that the distribution of bed-based accessibility in primary hospitals was more uneven. From the perspective of partition type, the accessibility of different levels of hospitals in the urban-rural division is higher than that under the three major divisions. For example, the inequality value within the physician-based accessibility group of primary hospitals was 0.507 in the urban-rural division and 0.3959 in the three major divisions. This indicates that the inequality of hospital accessibility distribution is more significant within urban-rural areas. At the same time, the accessibility of different levels of hospitals in the urban-rural division is lower than that of the three major divisions. For example, the inter-group inequality of bed accessibility in tertiary hospitals was 0.0953 in the urban-rural divide and 0.1314 in the three major divisions. The complex overlapping of accessibility distributions under urban-rural divisions significantly impacts overall disparities. The accessibility distribution under urban-rural divisions does not follow a simple binary structure of high in cities-low in rural areas, and there may be secondary differences within urban areas and rural regions [ 9 ]. Intra-group inequality under the urban-rural division generally exceeds that of the three major regional partitions across all hospital levels, which indicated that intra-group inequality (such as resource inequality within cities or rural areas) are the main sources of overall disparities, rather than the traditionally perceived inter-group inequality between urban and rural areas [ 32 ]. At the same time, the contribution rate of inter-group could not be ignored, and its value was 29.3%-47.7%, indicating that the unequal distribution of accessibility across hospital levels had a significant impact on the overall inequality. Inevitably, this study also has some limitations. Based on the actual situation of the study area and relevant national medical reform policies, this study refers to relevant literature and sets different search radii according to the grades of hospitals. Although this study takes into account the attractiveness of the hospital's grade and scale to residents, as well as the impact of the distance decay factor on residents' trips to hospitals, it fails to consider the actual travel willingness of residents. At the same time, this study focuses on the accessibility of real-time traffic, but only includes the self-driving mode. In fact, public transportation in Chongqing is also an important choice for many residents to travel, and public transportation needs to be incorporated into the study in future research. 5. Conclusions This research adopts the G2SFCA technique and combines it with the real-time dynamic traffic information obtained through the Amap API, so as to conduct a hierarchical evaluation of the accessibility of public medical resources in Chongqing. Calculating the accessibility of medical resources through the number of hospital beds and the number of physicians at the facilities respectively reflects different dimensions of the supply of medical resources and their differential impacts on service capabilities. And the Dagum Gini coefficient is used to study regional and urban-rural disparities in healthcare accessibility. This study can draw the following conclusions. There is obvious inequality in the spatial accessibility of medical and health services at different levels in Chongqing. The accessibility of tertiary hospitals is the highest, while that of primary hospitals is the lowest. Whether it is the accessibility based on hospital beds or the accessibility based on physicians, the accessibility of hospitals of different levels in the city proper of Chongqing is much higher than that in the three gorges reservoir area in northeast Chongqing and the Wuling mountain area in southeast Chongqing. The average accessibility of hospitals of different levels in the three gorges reservoir area in northeast Chongqing and the Wuling mountain area in southeast Chongqing has a relatively small difference. From the perspective of districts and sub-districts, not all areas with high medical resource accessibility are situated in Chongqing's urban area. The correlation between bed-based and physician-based healthcare resource accessibility strengthened as the hospital hierarchy ascends. There are significant imbalances in the allocation of medical resources in public hospitals in Chongqing across different dimensions. The overall Gini coefficients for hospitals of different levels indicate that accessibility to primary hospitals is the most uneven, and the degree of accessibility inequality increases as hospital levels decrease. In terms of resource types, accessibility based on physicians in tertiary hospitals exhibits slightly higher inequality than that based on beds, while accessibility based on beds in primary hospitals is more unevenly distributed. Regarding zoning types, accessibility disparities among hospitals in urban-rural divisions are generally greater than those in the three major divisions. In terms of contribution ratios, intra-group contributions generally exceed inter-group contributions, but inter-group contributions also significantly impact overall inequality. Taking Chongqing as an example, this study on the spatial and urban-rural inequalities in the accessibility of hierarchical medical facilities in Chinese megacities can not only provide decision-making support for optimizing medical resource allocation in Chongqing, but also offer theoretical references and practical insights for other megacities with similar urban-rural spatial structures. Declarations Author contributions ** Conflict of interest: The authors declare no conflict of interest. Funding: **. Author Contribution Zhuolin Tao and Xingshu Chen designed the study, revised the manuscript, and gave final ap-proval for the submission. Chao Tan implemented the study and drafted the manuscript. Wenliang Zhang, Ran Zheng, Wei Zhang, Yu Chen and Caizhi Tang reviewed the manuscript. All authors read and approved the manuscript and participated sufficiently in, and stand by the validity of this work. Data availability statement: The dataset used and analyzed during the current study is available from the author upon reasonable request. References Tao, Z., Yao, Z., Kong, H., Duan, F., & Li, G. (2018). Spatial accessibility to healthcare services in Shenzhen, China: improving the multi-modal two-step floating catchment area method by estimating travel time via online map APIs. BMC Health Services Research , 18 . 10.1186/s12913-018-3132-8 World Health, O. (2015). 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Measuring Spatial Accessibility of Health Care Providers - Introduction of a Variable Distance Decay Function within the Floating Catchment Area (FCA) Method. Plos One , 11 , e0159148. 10.1371/journal.pone.0159148 Zhang, S., Song, X., & Zhou, J. (2021). An equity and efficiency integrated grid-to-level 2SFCA approach: spatial accessibility of multilevel healthcare. International Journal for Equity in Health , 20 . 10.1186/s12939-021-01553-9 Qiao, Q., Zhang, Y., Liu, J., Xu, H., & Gan, L. (2022). Health Care Accessibility Analysis Considering Behavioral Preferences for Hospital Choice. Applied Sciences , 12 . 10.3390/app12136822 Jin, T., Cheng, L., Wang, K., Cao, J., Huang, H., & Witlox, F. (2022). Examining equity in accessibility to multi-tier healthcare services across different income households using estimated travel time. Transport Policy , 121 , 1–13. https://doi.org/10.1016/j.tranpol.2022.03.014 Amap Open Platform (Accessed March 26, 2025). Geocoding/Reverse Geocoding. Availabe online: https://lbs.amap.com/api/webservice/guide/api/georegeo (accessed on. Chongqing Municipal Bureau of Statistics. the Seventh National Population Census (Accessed April 2, 2025). Availabe online: https://tjj.cq.gov.cn/zwgk_233/fdzdgknr/tjxx/sjzl_55471/pcsj/ (accessed on. Chen, Y., Xu, C., Ge, Y., Zhang, X., & Zhou, Y. (2024). A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data. Earth System Science Data , 16 , 3705–3718. 10.5194/essd-16-3705-2024 Tao, Z., Zhang, R., Liu, C., & Zhong, Q. (2025). On the Modifiable Areal Unit Problem (MAUP) in healthcare accessibility measurement via the two-step floating catchment area (2SFCA) method. Health & Place , 93 , 103468. https://doi.org/10.1016/j.healthplace.2025.103468 Amap Open Platform (Accessed April 14, 2025). Path Planning 2.0. Availabe online: https://lbs.amap.com/api/webservice/guide/api/newroute (accessed on. Zhang, S., Song, X., Wei, Y., & Deng, W. (2019). Spatial Equity of Multilevel Healthcare in the Metropolis of Chengdu, China: A New Assessment Approach. International Journal of Environmental Research and Public Health , 16 . 10.3390/ijerph16030493 Fu, L. (2021). Spatial accessibility and influencing factors of medical service in ountainous Cities-Take the central urban area of Chongqing as an example. Doctoral dissertation. Varkey, A., & Haridas, H. N. (2025). Comparison of Income Inequality Among Indian States Using Quantile Functions. Computational Economics . 10.1007/s10614-025-10880-w Wang, Z., Dong, L., Xing, X., Liu, Z., & Zhou, Y. (2023). Disparity in hospital beds’ allocation at the county level in China: an analysis based on a Health Resource Density Index (HRDI) model. BMC Health Services Research , 23 . 10.1186/s12913-023-10266-4 Jiang, Y., Cai, X., Wang, Y., Dong, J., & Yang, M. (2023). Assessment of the supply/demand balance of medical resources in Beijing from the perspective of hierarchical diagnosis and treatment. Geospatial Health , 18 . 10.4081/gh.2023.1228 Cui, C., Zuo, X., Wang, Y., Song, H., Shi, J., & Meng, K. (2020). A comparative study of patients' satisfaction with different levels of hospitals in Beijing: why do patients prefer high-level hospitals? BMC Health Service Research , 20 , 643. 10.1186/s12913-020-05507-9 Neudorf, J. (2014). Understanding Accessibility, Analyzing Policy New Approaches for a New Paradigm. Li, Z., Yang, L., Tang, S., & Bian, Y. (2020). Equity and Efficiency of Health Resource Allocation of Chinese Medicine in Mainland China: 2013–2017. Frontiers in Public Health , 8 . 10.3389/fpubh.2020.579269 Dong, E., Xu, J., Sun, X., Xu, T., Zhang, L., & Wang, T. (2021). Differences in regional distribution and inequality in health-resource allocation on institutions, beds, and workforce: a longitudinal study in China. Archives of Public Health , 79 . 10.1186/s13690-021-00597-1 Additional Declarations No competing interests reported. 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14:30:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":446121,"visible":true,"origin":"","legend":"\u003cp\u003eLocation and administrative divisions of Chongqing\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7421555/v1/573270263bc200f67d20af1c.png"},{"id":91352078,"identity":"90fcde9e-24b9-48ec-9a27-44d0e9082920","added_by":"auto","created_at":"2025-09-15 14:46:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":530532,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of population and hospitals in Chongqing\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7421555/v1/ce93540b9e85325b3afd28b1.png"},{"id":91350314,"identity":"ea161a77-a540-42ee-ab4b-462509887703","added_by":"auto","created_at":"2025-09-15 14:30:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1077582,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of accessibility for tertiary hospitals (A:bed-based accessibility, B: physician-based accessibility)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7421555/v1/1a3f8b7f06302e03227f5979.png"},{"id":91350315,"identity":"161e816b-04b9-4224-b925-dc127a0105f0","added_by":"auto","created_at":"2025-09-15 14:30:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1097021,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of accessibility for secondary hospitals (A:bed-based accessibility, B: physician-based accessibility)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7421555/v1/9be4b3ea0719211659bdd3ec.png"},{"id":91352077,"identity":"76dfc0d8-38fe-4352-960b-d955d76abf5e","added_by":"auto","created_at":"2025-09-15 14:46:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":997884,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of accessibility for primary hospitals (A:bed-based accessibility, B: physician-based accessibility)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7421555/v1/56c698a564e93cbee3c01579.png"},{"id":91353220,"identity":"6340ebb4-184b-4ec3-9d0a-d32413487ee2","added_by":"auto","created_at":"2025-09-15 14:55:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5006148,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7421555/v1/016eab6c-9fcb-4df8-856b-ebf7a82c895f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-scale inequalities in accessibility of hierarchical medical facilities in Chongqing, China: a comprehensive assessment of physician and bed resources","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe fairness and accessibility of medical services have become one of the core objectives in the construction of the national medical and health system under the guidance of \u0026ldquo;Healthy China 2030 Planning Outline\u0026rdquo; [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In particular, since the proposal of the hierarchical medical system in 2015, the accessibility of medical facilities at different levels has attracted significant attention from both academia and policymakers [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, existing research has significant limitations. First, existing studies have mostly focused on facilities at a single scale or a single level [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with insufficient cross-analysis between \"hierarchical facilities\" (tertiary hospital, secondary hospital, and primary hospital) and \"multi-scale inequalities\" (three major divisions and urban-rural divisions). Second, in terms of medical resource factors, most research centers on either the number of beds or the number of physicians, with insufficient consideration of the impact of both the number of beds and physicians on the accessibility of hierarchical medical facilities [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe acceleration of China's urbanization process and the rise of megacities have made the spatial allocation of medical resources increasingly complex. Data from the National Bureau of Statistics shows that, China had 11 megacities defined by a permanent population of over 10\u0026nbsp;million as of 2023. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among them, megacities such as Chongqing, Beijing, and Shanghai exhibit a composite spatial structure of large cities and vast rural areas, forming complex coupling relationships among urban-rural population mobility [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], regional economic disparities, and medical resource allocation. Existing studies on medical accessibility in Chinese cities, such as analyses of Beijing and Shanghai, have revealed the spatial inequality pattern of medical resource agglomeration in central urban areas and relative scarcity in suburban areas [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the research boundaries are mostly limited to urban areas, failing to deeply analyze the structural inequality of multi-scale medical service systems within urban regions and their impacts on accessibility. From an international perspective, studies on the Tokyo Metropolitan Area and Greater London have focused on the spatial allocation of medical resources in metropolitan areas [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], but they lack targeted discussions on urban regions in developing countries with prominent urban-rural binary structures.\u003c/p\u003e\u003cp\u003eSince spatial analysis techniques were introduced into the field of medical geography in the 1970s, extensive research in China and abroad has explored the spatial distribution and accessibility of medical facilities. Early studies predominantly analyzed the distribution characteristics of medical resources based on static supply-demand ratios and distance-decay models [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. With the innovation of GIS technology and spatial analysis methods, accessibility metrics based on time costs, such as the two-step floating catchment area (2SFCA) method and network analysis models, have gradually become mainstream [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As an improved spatial analysis method, the Gaussian two-step floating catchment area (G2SFCA) approach possesses unique advantages in researching medical resource accessibility [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Building on the traditional 2SFCA method, this approach simulates the distance-decay of medical service attractiveness by introducing a Gaussian function. Compared with the traditional 2SFCA, it more realistically reflects the spatial scope of medical service influence in reality and more accurately assesses the accessibility levels of medical resources at different spatial locations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, existing studies often treat medical resources as homogeneous elements, rarely distinguishing the service capability inequality among medical institutions of different levels, such as tertiary hospitals, secondary hospitals, and primary hospitals, as well as the heterogeneous roles of bed and physician resources in service provision.\u003c/p\u003e\u003cp\u003eAs the sole municipality directly under the Central Government in China\u0026rsquo;s central and western regions, Chongqing integrates a large urban agglomeration with vast rural areas, featuring a prominent urban-rural binary structure and evident spatial heterogeneity in medical resource allocation. Taking Chongqing as a typical case, this study focuses on the multi-scale inequalities in the accessibility of hierarchical medical facilities within Chinese megacity regions, aiming to address the following issues. First, breaking through the limitation that existing studies mostly focus on facilities at a single scale or a single level, this study, based on the accessibility of hierarchical medical facilities (tertiary hospital, secondary hospital, and primary hospital), examines the multi-scale inequalities (three major divisions and urban-rural divisions). Second, on the basis of considering both physicians and beds, through in-depth analysis of spatial inequality and urban-rural inequality, it systematically interprets the characteristics of medical accessibility in different urban divisions and urban-rural areas. Selecting Chongqing as the research object can not only provide decision-making support for optimizing medical resource accessibility in Chongqing but also offer practical insights for other megacities with similar urban-rural spatial structures.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study area\u003c/h2\u003e\u003cp\u003eChongqing is located in southwest China and has jurisdiction over 38 districts and counties, such as Shapingba, Yubei, and Youyang (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Chongqing is a typical mountainous city, with a total area of 82,400 km\u003csup\u003e2\u003c/sup\u003e, and 76% of the total area is mountainous (8). According to the 2024 Statistical Yearbook, Chongqing has a permanent population of 31,914,300. According to the \u0026ldquo;Chongqing Territorial Space Master Plan (2021\u0026ndash;2035)\u0026rdquo;, Chongqing is divided into the city proper of Chongqing, the three gorges reservoir area in northeast Chongqing, and the Wuling mountain area in southeast Chongqing. The city proper of Chongqing is located in the central and western part of Chongqing, which has relatively flat terrain, developed economy and high level of urbanization. On the contrary, the three gorges reservoir area in northeast Chongqing and the Wuling mountain area in southeast Chongqing are dominated by large areas of mountainous land, with relatively low urbanization level and mainly rural population. At the same time, this study defined streets as urban areas and townships as rural areas to investigate inequalities in access to healthcare between urban and rural areas.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data and pre-processing\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Medical facility data\u003c/h2\u003e\u003cp\u003eThe medical facility data was sourced from the Chongqing Health Commission. It mainly included hospital names, economic types, ratings, addresses, the number of practicing (assistant) physicians, and the number of actual beds. This study mainly considered 227 public hospitals in Chongqing (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These hospitals are classified into tertiary hospital, secondary hospital, primary hospital, and unrated hospital according to the hospital grades. Considering that the functions and other aspects of primary and unrated hospitals are relatively similar, primary and unrated hospitals are combined into primary hospitals. Among the 227 public hospitals, there are 89 tertiary hospitals, 80 secondary hospitals, and 57 primary hospitals. In total, there are 43,628 physicians and 135,468 hospital beds. The hospital address data were transformed to geographic spatial data based on the Amap geocoding API [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and the calculations were executed by coding in Python 3.7 (manufacturer, city, state abbreviation if USA, country Guido van Rossum, Netherlands).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Permanent resident population data\u003c/h2\u003e\u003cp\u003eThe data of the permanent resident population was sourced from the data of the Seventh National Population Census conducted by the Chongqing Municipal Bureau of Statistics [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and has been adjusted using the data of each district and county in the Chongqing Statistical Yearbook 2024. The 100-m gridded population dataset of China\u0026rsquo;s seventh census was sourced from the data shared by Professor Chen Yuehong's team on the figshare platform [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The spatial scope of this dataset covers the whole country, and the data year is 2020. The spatial resolution is 100 meters, and the data coordinate system is Albers_Conic_Equal_Area. The data format is raster data, and the raster value represents the population number of each grid (with an approximate area of 100m*100m). This dataset was used to calculate the population-weighted centroids as the population center of streets or towns for the calculation of accessibility [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Real-time travel time data\u003c/h2\u003e\u003cp\u003eReal-time travel time were obtained from the Amap route optimization service API v2.0 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which based on comprehensive road network information and combines real-time traffic conditions to provide users with accurate route optimization capabilities across multiple terminals. The navigation service of Amap provides route planning, mileage, and travel time. And its travel modes include driving route optimization, public transportation route optimization, cycling route optimization, and walking route optimization. Driving route optimization was used to calculate the travel time from each population-weighted centroid to a center level of hospital in this paper. To circumvent peak traffic periods, holidays, and other exceptional circumstances, the calculation was carried out between 9:00 and 12:00 a.m. on weekdays from April 14 to 29, 2025 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Methods\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Calculate the weighted population centroid\u003c/h2\u003e\u003cp\u003eThe population-weighted centroid refers to the centroid position of a region when the population distribution is taken into account. In practical research, calculating accessibility using the geometric centers of streets often overlooks the heterogeneity of population distribution, leading to deviations in the calculation results [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, it is more accurate to calculate spatial accessibility using the weighted population centroid. The 100-m gridded population dataset of China\u0026rsquo;s seventh census was used to calculate the population-weighted centroids as the population center of streets or towns for the calculation of accessibility. The formula for calculating the weighted population centroid is as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\overline{X}=\\:\\frac{{\\sum\\:}_{i=1}^{n}{P}_{i}{X}_{i}}{{\\sum\\:}_{i=1}^{n}{P}_{i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\overline{Y}=\\:\\frac{{\\sum\\:}_{i=1}^{n}{P}_{i}{Y}_{i}}{{\\sum\\:}_{i=1}^{n}{P}_{i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\overline{X}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\overline{Y}\\)\u003c/span\u003e\u003c/span\u003e are respectively the abscissa and ordinate of the population-weighted centroid. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e respectively represent the geographical coordinates of the \u003cem\u003ei\u003c/em\u003e area. \u003cem\u003en\u003c/em\u003e represents the number of streets or townships in Chongqing. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the population quantity of the \u003cem\u003ei\u003c/em\u003e street. The population-weighted centroids were calculated by using ArcGIS Pro (Esri, Redlands, CA, USA), and its coordinates were converted to GCJ-02 using Python 3.7, aiming to unify the coordinate systems for calculating travel time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Calculate the spatial accessibility\u003c/h2\u003e\u003cp\u003eG2SFCA is an improved accessibility model based on the traditional 2SFCA method by introducing Gaussian kernel functions. Its core lies in the 2SFCA method, first determining the spatial scope of the facility, and then using the Gaussian equation to empower within this range, and finally calculating the accessibility index of the facility\u0026zwnj;. The steps are as follows. In the first step, all demand nodes within the catchment area of each facility are searched for, and then the supply-demand ratio for each facility is calculated [1]. The formula can be written as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{R}_{j}=\\frac{{S}_{j}}{\\sum\\:_{k=1}^{m}{D}_{k}\\times\\:f\\left({d}_{ij}\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:f\\left({d}_{ij}\\right)=\\left\\{\\begin{array}{c}\\frac{{e}^{-1/2\\times\\:({{d}_{ij}/{d}_{0})}^{2}-}{e}^{-1/2}\\:\\:}{1-{e}^{-1/2}\\:\\:},\\:\\:\\:\\\\\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:0,\\:\\:\\:\\:\\:\\:\\:{\\:\\:d}_{ij}\u0026gt;{d}_{0}\\end{array}\\right.{d}_{ij}\\le\\:{d}_{0}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{j}\\)\u003c/span\u003e\u003c/span\u003e is supply to demand ratio, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{j}\\)\u003c/span\u003e\u003c/span\u003e is the capacity of supply at location \u003cem\u003ej\u003c/em\u003e, the number of hospital beds and physicians are used to represent it. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{k}\\)\u003c/span\u003e\u003c/span\u003e is the demand amount, which is represented by the population quantity in the region. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{ij}\\:\\)\u003c/span\u003e\u003c/span\u003eis the travel time between \u003cem\u003ej\u003c/em\u003e and \u003cem\u003ei\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\left({d}_{ij}\\right)\\)\u003c/span\u003e\u003c/span\u003e is distance decay function, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the size of catchment area. In this study, different search radii \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{0}\\)\u003c/span\u003e\u003c/span\u003e are established according to the varying grades of hospitals. Specifically, the service radius of hospitals that of primary hospitals is set at 60 minutes, that of secondary hospitals is set at 90 minutes, and that of tertiary hospitals is set at 150 minutes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the second step, the supply-demand ratios of all facilities within the catchment area of each demand node are summed up. The sum of the supply-demand ratios for each demand node serves as its spatial accessibility score. The formula can be written as:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{A}_{i}=\\sum\\:_{j=1}^{n}{R}_{j}\\times\\:f\\left({d}_{ij}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the accessibility of medical services at demand node \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Calculating the equitability of healthcare allocation\u003c/h2\u003e\u003cp\u003eThe Dagum Gini coefficient, an extended model of the Gini coefficient, is a statistical method proposed by Italian economist Carmelo Dagum for analyzing inequality in income or resource allocation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Compared with the traditional Gini coefficient, the Dagum Gini coefficient can not only measure the overall inequality level but also decompose inequality into intra-group inequality, inter-group inequality, and hypervariable density. The Dagum Gini coefficient is used to measure the equality of resource allocation. The closer it is to 0, the fairer the allocation; the closer it is to 1, the more unfair the allocation. Intra-group inequality reflects the resource disparities among hospitals within the same division; Inter-group inequality embodies the resource inequality between different divisions; Hyper-variable density contribution demonstrates the complex overlapping patterns of resource distribution; and the respective contribution percentages indicate the degree to which each component influences the overall Gini coefficient. The specific formula is:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:G=\\frac{{\\sum\\:}_{j=1}^{k}{\\sum\\:}_{i=1}^{{n}_{j}}{\\sum\\:}_{r=1}^{{n}_{h}}\\left|{x}_{ji}-{x}_{hr}\\right|}{2{n}^{2}\\stackrel{-}{y}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cem\u003eG\u003c/em\u003e is the overall Gini coefficient, \u003cem\u003ek\u003c/em\u003e is the number of streets or townships in Chongqing, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{j}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{h}\\)\u003c/span\u003e\u003c/span\u003e respectively represent the sample sizes of different division j and h, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{ji}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{hr}\\)\u003c/span\u003e\u003c/span\u003e respectively represent the individual values in different division \u003cem\u003ej\u003c/em\u003e and \u003cem\u003eh\u003c/em\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{y}\\)\u003c/span\u003e\u003c/span\u003e is the sample mean in the division. The Dagum Gini coefficient can be further decomposed into three components. The formula is:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:G={G}_{w}+{G}_{nb}+{G}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{w}\\)\u003c/span\u003e\u003c/span\u003e is the contribution of intra-group inequality. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{nb}\\)\u003c/span\u003e\u003c/span\u003e is the contribution of inter-group inequality. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the intensity of cross-region variation.\u003c/p\u003e\u003cp\u003eThis study was based on the accessibility of public hospitals in Chongqing. It calculated the Dagum Gini coefficients and their decompositions under different hospital levels (tertiary, secondary, primary), division types (three major divisions, urban - rural divisions), and per capita medical resources (physicians, beds).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Spatial accessibility of tertiary hospitals and its inequality\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Spatial accessibility of tertiary hospitals\u003c/h2\u003e\u003cp\u003eThe spatial accessibility of tertiary hospitals based on the number of hospital beds were calculated with a service radius defined by a 150-minute travel time threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The range of spatial accessibility for per thousand persons in Chongqing is 0-4.552, which indicates that the accessibility of medical resources in Chongqing based on bed capacity exhibits obvious spatial inequality. From the perspective of the regional division, the city proper of Chongqing ranks the highest has the highest value (3.078), followed by the Wuling mountain area in southeast Chongqing (2.16), and the three gorges reservoir area in northeast Chongqing has the lowest value (1.916). When looking at each district and county in Chongqing, the top three districts or counties are Jiangbei (4.243), Nanan (4.176), Dadukou (3.995), respectively, and the three districts or counties with the lowest average accessibility are Chengkou (0. 512), Wushan (1.277), Fengjie (1.357), respectively. When analyzed at the sub-district and township scale, overall, the accessibility decreases gradually from the central city to the surrounding areas. The top three areas with the highest accessibility of medical resources are Sanhe (4.552) and Daxie (4.409) in Shizhu, Heixi (4.401) in Qiangjian, respectively, while the three areas with the lowest overall accessibility (the value is 0) are Houping and Gaonan in Chengkou, Peishi, Duping and Dengjia in Wushan, respectively.\u003c/p\u003e\u003cp\u003eThe spatial accessibility of tertiary hospitals based on physicians were calculated with a service radius defined by a 150-minute travel time threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The spatial accessibility for per thousand persons of medical resources in Chongqing spans from 0 to 1.577. This further demonstrates, in the context of physician-based availability, that significant variations exist in the accessibility of medical resources across the city. From the perspective of the regional divisions, the accessibility of medical resources based on physician exhibits the similar characteristics to that based on bed capacity. the city proper of Chongqing ranks the highest has the highest value (1.117), followed by the Wuling mountain area in southeast Chongqing (0.782), and the three gorges reservoir area in northeast Chongqing has the lowest value (0.569). Among the districts or counties of Chongqing, Jiangbei (1.532), Nanan (1.516), and Dadukou (1.466) take the top three spots, and Chengkou (0.136), Wushan (0.346), and Fengjie (0.386) are the three districts and counties with the lowest average accessibility. When analyzed at the sub-district and township scale, the top three areas with the highest accessibility of medical resources are Guojiatuo (1.576) in Jiangbei, Heixi (1.574) in Qiangjian, Yuzui (1.563) in Jiangbei, respectively, while the districts or counties with low values of accessibility both based on physician and bed capacity are the same, and the values of accessibility in these districts or counties are all zero.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Pearson correlation coefficient was calculated using bed-based accessibility and physician-based accessibility of tertiary hospitals at different scales. From the perspective of counties and districts, The Pearson correlation coefficient was 0.987 and a P-value below 0.01. When analyzed at the sub-district and township scale, the Pearson correlation coefficient was 0.982 and a P value below 0.01. This indicates a high degree of correlation between the accessibility of tertiary hospitals calculated based on bed resources and that based on physician resources.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Multi-scale inequality in accessibility of tertiary hospitals\u003c/h2\u003e\u003cp\u003eThe Dagum Gini coefficient of accessibility for tertiary hospitals was calculated under different medical resources (beds, physicians) and different regional divisions (three major divisions, urban-rural divisions) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Across both the three major divisions and the urban-rural divisions, the overall Gini coefficient for bed-based accessibility was 0.2553, which was lower than that for physician-based accessibility (0.2756). This suggested a more uneven distribution of physician-based accessibility among tertiary hospitals. From the perspective of the three major divisions, the intra-group inequality value of bed-based accessibility was 0.1418, the inter-group inequality value was 0.1135, and the hyper-variable density value was 0, accounting for 55.5%, 44.5%, and 0% respectively. Meanwhile, the intra-group inequality value based on physician accessibility was 0.1442, the inter-group inequality value was 0.1314, and the super-variation density value also was 0, with their respective proportions being 52.3%, 47.7%, amd 0%. From the perspective of the urban-rural divisions, the intra-group inequality value of bed-based accessibility was 0.1771, the inter-group inequality value was 0.0782, and the hyper-variable density value was 0, with their respective proportions amounting to 69.4%, 30.6%, and 0%. Meanwhile, the intra-group inequality value based on physician accessibility was 0.1903, the inter-group inequality value was 0.0853, and the super-variation density value also was 0, with their respective proportions being 69%, 31%, and 0%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Dagum Gini coefficients and their decompositions of tertiary hospitals under different dimensions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivision type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResource type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall Gini coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntra-\u003c/p\u003e\u003cp\u003egroup inequality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInter-\u003c/p\u003e\u003cp\u003egroup inequality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHyper-\u003c/p\u003e\u003cp\u003evariable density\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIntra-\u003c/p\u003e\u003cp\u003egroup contribution (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInter-\u003c/p\u003e\u003cp\u003egroup contribution (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHyper-\u003c/p\u003e\u003cp\u003evariable density contribution (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree-major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e44.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree-major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e47.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban-rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban-rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Spatial accessibility and its inequality of secondary hospitals\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Spatial accessibility of secondary hospitals\u003c/h2\u003e\u003cp\u003eUtilizing a 90-minute travel time criterion to define the service radius, the spatial accessibility of secondary hospitals determined by the number of hospital beds was calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Its accessibility range is 0-2.869, which is far lower than the accessibility of tertiary hospitals in terms of bed capacity. From the perspective of regional divisions, the ranking of the three regions is consistent with that of tertiary hospitals. The city proper of Chongqing ranks the highest has the highest value (0.872), followed by the Wuling mountain area in southeast Chongqing (0.673), and the three gorges reservoir area in northeast Chongqing has the lowest value (0.64). When examining each district and county in Chongqing, Chengkou, boasting a value of 1.919, Dadukou with a value of 1.433, and Nanan with a value of 1.314 are respectively the top three districts or counties. Meanwhile, Yunyang, having a value of 0.244, Shizhu with a value of 0.34, and Tongnan with a value of 0.349 are respectively the three districts or counties with the lowest average accessibility. When conducting an analysis at the sub-district and township scale, the three areas that rank at the top are Gecheng (2.869), Fuxing (2.86) and Longtian (2.792) in Dazu. Moreover, the accessibility of 66 sub-districts or townships is 0, and they are distributed in 30 districts and counties such as Yubei, Wanzhou, Youyang, etc.\u003c/p\u003e\u003cp\u003eThe spatial accessibility of secondary hospitals based on the number of physicians is calculated with a service radius of 90 minutes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The scope of accessibility is 0-0.74. From the perspective of regional division, the city proper of Chongqing ranks the highest (0.213), followed by the three gorges reservoir area in northeast Chongqing (0.166), and finally the Wuling mountain area in southeast Chongqing (0.157). In the various districts and counties of Chongqing, Chengkou (0.492), Dadukou (0.389), and Jiulongpo (0.349) occupy the top three positions, while Fengdu (0.056), Tongnan (0.06), and Shizhu (0.078) have the lowest average levels of medical resource accessibility. Upon analysis at the sub-district and township level, the top three rankings for both physician-based and bed-based medical resource accessibility are identical. Similarly, 66 sub-districts or townships exhibit zero accessibility.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Pearson correlation coefficient was calculated using bed-based accessibility and physician-based accessibility of secondary hospitals at different scales. From the perspective of counties and districts, The Pearson correlation coefficient was 0.888 and a P-value below 0.01. When analyzed at the sub-district and township scale, the Pearson correlation coefficient was 0.913 and a P-value equaled 0. This suggested a marginally lower degree of correlation between the accessibility of secondary hospitals calculated based on bed resources and that based on physician resources.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Multi-scale inequality in accessibility of secondary hospitals\u003c/h2\u003e\u003cp\u003eThe Dagum Gini coefficient of accessibility for secondary hospitals was calculated under different medical resources (beds, physicians) and different regional divisions (three major divisions, urban-rural divisions) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In both the three major divisions and the urban-rural divisions, the overall Gini coefficient for bed-based accessibility was 0.3744, which was lower than that for physician-based accessibility (0.3968). This slao suggested a more uneven distribution of physician-based accessibility among secondary hospitals. From the perspective of the three major divisions, the intra-group inequality value of bed-based accessibility was 0.2193, the inter-group inequality value was 0.1541, and the hyper-variable density value was 0.001, accounting for 58.6%, 41.2%, and 0.3% respectively. Meanwhile, the intra-group inequality value based on physician accessibility was 0.2352, the inter-group inequality value was 0.1606, and the super-variation density value also was 0.001, with their respective proportions being 59.3%, 40.5%, amd 0.3%. From the perspective of the urban-rural divisions, the intra-group inequality value of bed-based accessibility was 0.2645, the inter-group inequality value was 0.1099, and the hyper-variable density value was 0, with their respective proportions amounting to 70.7%, 29.3%, and 0%. Meanwhile, the intra-group inequality value based on physician accessibility was 0.2739, the inter-group inequality value was 0.1229, and the super-variation density value also was 0, with their respective proportions being 69%, 31%, amd 0%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Dagum Gini coefficients and their decompositions of secondary hospitals under different dimensions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivision type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResource type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall Gini coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntra-\u003c/p\u003e\u003cp\u003egroup inequality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInter-\u003c/p\u003e\u003cp\u003egroup inequality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHyper-\u003c/p\u003e\u003cp\u003evariable density\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIntra-\u003c/p\u003e\u003cp\u003egroup contribution (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInter-\u003c/p\u003e\u003cp\u003egroup contribution (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHyper-\u003c/p\u003e\u003cp\u003evariable density contribution (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree-major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e41.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree-major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e59.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e40.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban-rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e70.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban-rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Spatial accessibility and its inequality of primary hospitals\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Spatial accessibility of primary hospitals\u003c/h2\u003e\u003cp\u003eWith a 60-minute travel time used to demarcate the service radius, the spatial accessibility of primary hospitals gauged by the number of hospital beds available was calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The range of accessibility is 0-1.578. The area with the highest accessibility is three gorges reservoir area in northeast Chongqing (0.127), and its distribution is obvious regional inequality. Next comes the the city proper of Chongqing (0.114), where there is relatively uniform. The accessibility in the Wuling mountain in southeast Chongqing region is very low (0.001). Among all the districts and counties in Chongqing, the accessibility of Jiangjin (0.328), Wuxi (0.318), and Yunyang (0.315) rank in the top three positions. In contrast, Youyang, Pengshui, Xiushan, Qiangjiang and Chengkou are the five districts and counties with the accessibility value of zero. When the accessibility of medical resources is examined at the sub-district and township level, Shuangjiang in Yunyang (1.578), Huangshi (1.432), and Renhe (1.391) emerge as the top three regions with the highest levels of medical resource accessibility. Additionally, 429 sub-districts and townships exhibit zero medical resource accessibility. These areas are scattered across 38 districts and counties.\u003c/p\u003e\u003cp\u003eEmploying a 60-minute service radius, the spatial accessibility of primary hospitals measured by the number of physicians is computed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The accessibility range amounts to 0-0.081. From the perspective of regional division, the accessibility rankings and spatial distributions based on the number of physicians and based on the number of hospital beds exhibit a different characteristic. The city proper of Chongqing ranks the highest (0.021), followed by the three gorges reservoir area in northeast Chongqing (0.009), and finally the Wuling mountain area in southeast Chongqing (0). Among all Chongqing's districts and counties, Dadukou (0.045), Changshou (0.042) and Jiangbei (0.037) are the leading three, showing a relatively good status. However, Youyang, Pengshui, Xiushan, Qiangjiang and Chengkou are the five districts and counties with the accessibility value of zero. When analyzing medical resource accessibility at the sub-district and township level, Dandu in Changshou (0.081), Lidu (0.08) and Chognyi (0.078) in Fuling are the top three regions with the highest accessibility.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Pearson correlation coefficient was calculated using bed-based accessibility and physician-based accessibility of primary hospitals at different scales. From the perspective of counties and districts, the Pearson correlation coefficient was 0.488 and a P-value equaled 0.019. When analyzed at the sub-district and township scale, the Pearson correlation coefficient was 0.62 and a P-value below 0.01. This indicated very low degree of correlation between the accessibility of primary hospitals calculated based on bed resources and that based on physician resources.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Multi-scale inequality in accessibility of primary hospitals\u003c/h2\u003e\u003cp\u003eThe Dagum Gini coefficient of accessibility for primary hospitals was calculated under different medical resources (beds, physicians) and different regional divisions (three major divisions, urban-rural divisions) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Whether it was the three major divisions or the urban-rural divisions, the overall Gini coefficient of bed-based accessibility was 0.7622, which was higher than that of physician-based (0.6918), indicating that the distribution of bed-based accessibility in primary hospitals was more uneven. From the perspective of the three major divisions, the intra-group inequality value of bed-based accessibility was 0.3959, the inter-group inequality value was 0.2827, and the hyper-variable density value was 0.0835, accounting for 51.9%, 37.1%, and 11% respectively. Meanwhile, the intra-group inequality value based on physician accessibility was 0.346, the inter-group inequality value was 0.2826, and the super-variation density value was 0.0632, with their respective proportions being 50%, 40.8%, amd 9.1%. From the perspective of the urban-rural divisions, the intra-group inequality value of bed-based accessibility was 0.507, the inter-group inequality value was 0.2282, and the hyper-variable density value was 0.027, with their respective proportions amounting to 66.5%, 29.9%, and 3.5%. Meanwhile, the intra-group inequality value based on physician accessibility was 0.4153, the inter-group inequality value was 0.2475, and the super-variation density value was 0.029, with their respective proportions being 60%, 35.8%, amd 4.2%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Dagum Gini coefficients and their decompositions of primary hospitals under different dimensions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivision type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResource type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOverall Gini coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntra-\u003c/p\u003e\u003cp\u003egroup inequality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInter-\u003c/p\u003e\u003cp\u003egroup inequality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHyper-\u003c/p\u003e\u003cp\u003evariable density\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eIntra-\u003c/p\u003e\u003cp\u003egroup contribution (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eInter-\u003c/p\u003e\u003cp\u003egroup contribution (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHyper-\u003c/p\u003e\u003cp\u003evariable density contribution (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree-major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e37.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree-major\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e40.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban-rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban-rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e35.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eTaking Chongqing as a case study, this study investigates the spatial and urban-rural inequalities in the accessibility of hierarchical medical facilities in Chinese megacities. In general, by setting the service radius of tertiary hospitals at 150 minutes, that of secondary hospitals at 90 minutes, and that of primary hospitals at 60 minutes to study the accessibility, it is found that the average accessibility of tertiary hospitals in Chongqing is the highest (2.486), followed by secondary hospitals (0.751), and finally primary hospitals (0.097). Compared with other cities, although there are inequality in the setting of the service radius, the research results also show that the accessibility of hospitals with higher grades is higher [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The accessibility of a hospital is the result of the combined effects of its strategic positioning, resource aggregation, geographical concentration, transportation facilities, and patient preferences [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Hospitals of higher grades have higher accessibility due to their advantageous conditions.\u003c/p\u003e\u003cp\u003eFrom the perspective of regional division, whether it is the accessibility based on hospital beds or that based on physicians, the accessibility of hospitals of different levels in the city proper of Chongqing is the highest. Previous studies, from the perspective of the equality of medical resource allocation, have found that there is a significant inequality in the allocation of medical resources in Chongqing [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There is a large quantity of high-quality medical resources in the city proper of Chongqing, which leads to the accessibility of medical resources in the main urban area being higher than that in other regions. This is consistent with the research findings of other cities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. There is a relatively small difference in the average accessibility between the three gorges reservoir area in northeast Chongqing and the Wuling mountain area in southeast Chongqing. This may have something to do with the number of hospitals, which is 162 in the city proper of Chongqing, much higher than in the three gorges reservoir area in northeast Chongqing (with 47 public hospitals) and in the wuling mountains in southeastern Chongqing (18 public hospitals). The accessibility of tertiary hospitals in the Wuling mountain area in southeast Chongqing is higher than that in the three gorges reservoir area in northeast Chongqing. The accessibility of secondary hospitals based on physicians in the three gorges reservoir area in northeast Chongqing is higher than that in the Wuling mountain area in southeast Chongqing, while the accessibility of secondary hospitals based on hospital beds in the three gorges reservoir area in northeast Chongqing is lower than that in the Wuling mountain area in southeast Chongqing. The accessibility of primary hospitals in the Wuling mountain area in southeast Chongqing is lower than that in the three gorges reservoir area in northeast Chongqing. Analyzed from the scale of districts and sub-districts, the districts or sub-districts with high values of medical resource accessibility are not all located in the urban area of Chongqing, which indicates that although economic factors are an important factor affecting the accessibility of medical resources, the accessibility of medical resources is affected by a combination of multiple factors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The study found that accessibility based on bed numbers and physician counts exhibits similar trends, indicating a high degree of correlation in the spatial distribution of these two resource elements. Moreover, this correlation tends increased with the escalation of hospital hierarchy. This phenomenon was most pronounced in tertiary hospitals, with an Pearson correlation coefficient exceeding 0.982 and a P-value equaling 0. Studies on traditional Chinese medicine resources in China also show that the fluctuations in the Gini coefficients of beds and health personnel are synchronized [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This may stem from the linked mechanism of bed-staffing in China's hospital resource allocation, leading to synchronous agglomeration or sparsity of both between regions.\u003c/p\u003e\u003cp\u003eThe Dagum Gini coefficient was calculated for different levels of hospitals (primary, secondary, and tertiary) under different medical resources (beds, physicians) and different regional divisions (three major divisions, urban-rural divisions). From the overall Gini coefficient of different levels of hospitals, it was found that the overall Gini coefficient of primary hospitals was 0.6918\u0026ndash;0.7622, that of secondary hospitals was 0.3744\u0026ndash;0.3968, and that of tertiary hospitals was 0.2553\u0026ndash;0.2756, indicating that the accessibility distribution of medical resources in primary hospitals was the most unbalanced, followed by secondary hospitals, and the accessibility distribution of tertiary hospitals was the most balanced. A possible reason is the insufficient number of primary hospitals, which is 57, while the numbers of secondary and tertiary hospitals are 80 and 89, respectively. Although tertiary hospital accessibility is relatively balanced, it should be noted that this relative balance may only be reflected in geographical spatial distribution, rather than equity in service utilization, for example, tertiary hospitals are concentrated in major cities, leading to limited actual utilization by rural/low-income groups [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. From the perspective of resource types, the characteristics of the overall Gini coefficient based on bed accessibility and based on physician accessibility are different in different levels of hospitals. Among them, the overall Gini coefficient of physician-based accessibility in tertiary and secondary hospitals was slightly higher than that of bed-based accessibility, indicating that the physician-based accessibility distribution in tertiary and secondary hospitals was relatively more unequal than that in bed-based accessibility. In contrast, the overall Gini coefficient of bed-based accessibility was 0.7622 in primary hospitals than that of physician-based (0.6918), indicating that the distribution of bed-based accessibility in primary hospitals was more uneven. From the perspective of partition type, the accessibility of different levels of hospitals in the urban-rural division is higher than that under the three major divisions. For example, the inequality value within the physician-based accessibility group of primary hospitals was 0.507 in the urban-rural division and 0.3959 in the three major divisions. This indicates that the inequality of hospital accessibility distribution is more significant within urban-rural areas. At the same time, the accessibility of different levels of hospitals in the urban-rural division is lower than that of the three major divisions. For example, the inter-group inequality of bed accessibility in tertiary hospitals was 0.0953 in the urban-rural divide and 0.1314 in the three major divisions. The complex overlapping of accessibility distributions under urban-rural divisions significantly impacts overall disparities. The accessibility distribution under urban-rural divisions does not follow a simple binary structure of high in cities-low in rural areas, and there may be secondary differences within urban areas and rural regions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Intra-group inequality under the urban-rural division generally exceeds that of the three major regional partitions across all hospital levels, which indicated that intra-group inequality (such as resource inequality within cities or rural areas) are the main sources of overall disparities, rather than the traditionally perceived inter-group inequality between urban and rural areas [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. At the same time, the contribution rate of inter-group could not be ignored, and its value was 29.3%-47.7%, indicating that the unequal distribution of accessibility across hospital levels had a significant impact on the overall inequality.\u003c/p\u003e\u003cp\u003eInevitably, this study also has some limitations. Based on the actual situation of the study area and relevant national medical reform policies, this study refers to relevant literature and sets different search radii according to the grades of hospitals. Although this study takes into account the attractiveness of the hospital's grade and scale to residents, as well as the impact of the distance decay factor on residents' trips to hospitals, it fails to consider the actual travel willingness of residents. At the same time, this study focuses on the accessibility of real-time traffic, but only includes the self-driving mode. In fact, public transportation in Chongqing is also an important choice for many residents to travel, and public transportation needs to be incorporated into the study in future research.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis research adopts the G2SFCA technique and combines it with the real-time dynamic traffic information obtained through the Amap API, so as to conduct a hierarchical evaluation of the accessibility of public medical resources in Chongqing. Calculating the accessibility of medical resources through the number of hospital beds and the number of physicians at the facilities respectively reflects different dimensions of the supply of medical resources and their differential impacts on service capabilities. And the Dagum Gini coefficient is used to study regional and urban-rural disparities in healthcare accessibility.\u003c/p\u003e\u003cp\u003eThis study can draw the following conclusions. There is obvious inequality in the spatial accessibility of medical and health services at different levels in Chongqing. The accessibility of tertiary hospitals is the highest, while that of primary hospitals is the lowest. Whether it is the accessibility based on hospital beds or the accessibility based on physicians, the accessibility of hospitals of different levels in the city proper of Chongqing is much higher than that in the three gorges reservoir area in northeast Chongqing and the Wuling mountain area in southeast Chongqing. The average accessibility of hospitals of different levels in the three gorges reservoir area in northeast Chongqing and the Wuling mountain area in southeast Chongqing has a relatively small difference. From the perspective of districts and sub-districts, not all areas with high medical resource accessibility are situated in Chongqing's urban area. The correlation between bed-based and physician-based healthcare resource accessibility strengthened as the hospital hierarchy ascends. There are significant imbalances in the allocation of medical resources in public hospitals in Chongqing across different dimensions. The overall Gini coefficients for hospitals of different levels indicate that accessibility to primary hospitals is the most uneven, and the degree of accessibility inequality increases as hospital levels decrease. In terms of resource types, accessibility based on physicians in tertiary hospitals exhibits slightly higher inequality than that based on beds, while accessibility based on beds in primary hospitals is more unevenly distributed. Regarding zoning types, accessibility disparities among hospitals in urban-rural divisions are generally greater than those in the three major divisions. In terms of contribution ratios, intra-group contributions generally exceed inter-group contributions, but inter-group contributions also significantly impact overall inequality.\u003c/p\u003e\u003cp\u003eTaking Chongqing as an example, this study on the spatial and urban-rural inequalities in the accessibility of hierarchical medical facilities in Chinese megacities can not only provide decision-making support for optimizing medical resource allocation in Chongqing, but also offer theoretical references and practical insights for other megacities with similar urban-rural spatial structures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003e\u003cb\u003eAuthor contributions\u003c/b\u003e\u003c/h2\u003e\u003cp\u003e**\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003e**.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhuolin Tao and Xingshu Chen designed the study, revised the manuscript, and gave final ap-proval for the submission. Chao Tan implemented the study and drafted the manuscript. Wenliang Zhang, Ran Zheng, Wei Zhang, Yu Chen and Caizhi Tang reviewed the manuscript. All authors read and approved the manuscript and participated sufficiently in, and stand by the validity of this work.\u003c/p\u003e\u003ch2\u003eData availability statement:\u003c/h2\u003e\u003cp\u003eThe dataset used and analyzed during the current study is available from the author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTao, Z., Yao, Z., Kong, H., Duan, F., \u0026amp; Li, G. (2018). 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Differences in regional distribution and inequality in health-resource allocation on institutions, beds, and workforce: a longitudinal study in China. \u003cem\u003eArchives of Public Health\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13690-021-00597-1\u003c/span\u003e\u003cspan address=\"10.1186/s13690-021-00597-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Medical accessibility, Multi-scale inequality, Hospital beds, Physicians, Urban-rural disparity","lastPublishedDoi":"10.21203/rs.3.rs-7421555/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7421555/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe accessibility of medical service is directly related to the quality of residents' lives and has attracted increasing concerns of both researchers and policymakers. However, previous studies have paid few attentions to the multi-scale inequalities in the accessibility of hierarchical medical facilities in megacities, and lacks comprehensive assessment of both physician and bed resources. Using Chongqing, China as the study area, this study applied the Gaussian-based two-step floating catchment area (G2SFCA) method to measure the accessibility of hierarchical medical facilities considering both physician and bed resources. The Dagum Gini coefficient was employed to decompose the inequality in medical accessibility across multiple scales (the three major divisions and urban-rural divisions). Results show that the average accessibility of tertiary hospitals is the highest and its distribution is the most equal, whereas the accessibility of primary hospitals has the lowest average value and the highest inequality. The bed-based and physician-based accessibility exhibit obvious differences. The Pearson correlation coefficients between two types of accessibility are 0.982, 0.913, and 0.62 for tertiary, secondary and primary hospitals, respectively. From the perspective of multi-scale inequalities, whether the urban-rural division or the three major regional partitions, the value of intra-group inequality exceeded the inter-group inequality across all hospital levels, which indicated that intra-group inequality are the main sources of overall disparities. This study can shed new lights on the compositions of the inequality in hierarchical medical accessibility, and highlights the necessity of comprehensively considering physician and bed resources in medical accessibility assessment.\u003c/p\u003e","manuscriptTitle":"Multi-scale inequalities in accessibility of hierarchical medical facilities in Chongqing, China: a comprehensive assessment of physician and bed resources","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 14:30:51","doi":"10.21203/rs.3.rs-7421555/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf3cad09-e8c5-401d-9720-28dc61c1e083","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"26244518032065923725706626617370168816","date":"2026-05-15T00:34:01+00:00","index":34,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-15T14:30:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-15 14:30:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7421555","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7421555","identity":"rs-7421555","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}