Unequal roles of cities in the inter-urban healthcare system

Unequal roles of cities in the inter-urban healthcare system | 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 Article Unequal roles of cities in the inter-urban healthcare system Pengjun Zhao, Juan Li, Mengzhu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4837017/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2025 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Abstract Cities are increasingly interdependent regarding healthcare provision/demand. However, the inter-urban healthcare system (IHS) behind the nationwide patient mobility remains largely unknown. Leveraging human mobility big data, we reveal cities’ roles in providing/demanding quality healthcare within the IHS of China. We find that 8%of Chinese cities arenational and regional hubs that address the healthcare shortage of cities deprived of quality healthcare, while 63% of the cities that are unnoticed compensate for migrant workers being denied healthcare rights in megacities. IHS generates new structural inequalities in healthcare access exhibiting a Matthew effect, where the few (12%) cities that are already rich in healthcare resources benefit more and can strengthen their advantages in providing healthcare to local populations (32% of China’s total population). While, the majority (35%) of cities, particularly those facing healthcare shortages, are further disadvantaged in ensuring adequate healthcare for their local populations (26% of China’s total population). Social science/Geography Scientific community and society/Geography healthcare access patient mobility urban system role of cities human mobility Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cities are increasingly important in shaping people’s health outcomes 1 . Not only are they often the epicenters of disease origins and transmissions 2-3 , but they are also crucial in delivering healthcare services 4 . While cities have long been recognized as providers of healthcare to their local populations 4 , there is a growing trend of people seeking healthcare outside their city of residence 5-6 . Under the fast urbanization, the scarcity and uneven distribution of healthcare services, particularly high-quality ones, is common and cause the worldwide patient mobility between cities, regions and national jurisdictions 5-7 , as found in the US, Turkey, Italy, Iran, Laos, Thailand, India, and China 7-12 . Cities have thus developed the function of providing healthcare to non-local residents. The resulting inter-dependence of healthcare provision/demand among cities indicates the formation of what we term an inter-urban healthcare system (IHS). The hidden IHS can be explored through the lens of patient mobility 6-8 . Previous studies have examined which groups are moving to obtain non-local healthcare and why 6, 10 , and the size, networks, and temporal-geographical patterns of patient mobility 11-12 . Early studies considered patient mobility to be detrimental to healthcare access equity by exposing social disparities in the ability to travel for medical care 5-6 , and the underlying multiple purposes, which range from the wealthy population’s desire for the best treatment/diagnosis to the deprived populations’ needs for better or even basic ones 6 . Recent studies have pivoted to understand patient mobility as conducive to the improvement of healthcare efficiency and equity, especially repairing the spatial mismatch of healthcare provision and demand 7 . This shift guides the current policy framework towards encouraging patient mobility. For instance, the EU launched a Directive to secure patients’ rights to utilize healthcare in other EU countries 13 ; China launched national reforms to remove the institutional barriers to cross-city healthcare utilization and announced a regional healthcare center plan to encourage patients’ trips to nearby hub cities with adequate healthcare 14 . Nevertheless, little is known about how patient mobility with different purposes forms an IHS and the role of cities in it. Three questions remain to be answered: (1) How can the features of an IHS be identified through patient mobility? (2) What roles do the cities play in the IHS? and (3) How unequal are the roles of cities regarding healthcare access within the IHS? Leveraging the emerging human mobility big data 15-17 , this study answers the research questions and reveals the IHS in China. Three interrelated analyses constitute our inquiry. First, we identify nearly 2 million non-local patients’ visits to 1,404 first-class hospitals from over 300 million cellphone users’ daily movements in China, and we discern three types of patient mobility according to the purpose, especially including the pseudo-patient mobility, which refers to the temporary return of migrant workers from megacities to healthcare-deprived home cities for treatment/diagnosis. Second, based on a full picture of nationwide patient mobility, we classify the roles of Chinese cities in providing and demanding healthcare based mainly on the number and type of patients they host and lose. Third, we examine the inter-dependence among cities regarding healthcare provision/demand and unveil the hidden benefit structure regarding how cities exploit the IHS to offset/strengthen their structural (dis)advantages in local healthcare provision. The significance of this study is threefold (Fig. 1). First, we harness the potential of human mobility big data to discover the features of IHS by delivering a full picture of nationwide patient mobility, overcoming the selection bias of current studies that use incomplete healthcare records and questionnaire surveys 11, 18 . Second, we offer insights into the horizontal diversification of cities’ roles in urban systems under fast population mobility, i.e., inter-urban healthcare provision versus demand functions. It thus extends the current focus of urban system studies beyond focusing solely on economic interdependence and hierarchy 19-21 . Third, we deepen the understanding of healthcare access equity by combining the inequality of cities’ role in the IHS which provides valuable implications for planning healthcare resources, while previous studies have addressed the issue mainly from individual affordability, accessibility, capability, hospital locations and community contexts 22-25 . Results Three types of patient mobility interweave to form an inter-urban healthcare system We proposed a method to more fully identify cross-city healthcare utilization (see Methods) and found that three types of patient mobility interweave to form an IHS (Fig.2a). Primarily, mobility for better treatment/diagnosis accounts for 76% of all cross-city healthcare utilization, and only 5% are for the best treatment/diagnosis. The pseudo mobility of migrant workers accounts for 19%; they return to their healthcare-deprived home cities for treatment/diagnosis due to being excluded from the healthcare welfare system in the megacities 26 or due to lacking family support during the treatment 27 . In China, the hukou (household registration) system denies migrant workers full rights to local welfare 28-29 , and over 380 million of the population are migrant workers who often live in the megacities separately from their families 30 . Cities are not equally involved in the IHS as the origins and destinations of patient mobility (Fig.2a); 92% of 364 Chinese cities (336) are the origin only of the mobility for better treatment/diagnosis, while 13 cities are the origin both of the mobility for better treatment/diagnosis and pseudo mobility. Only 15 cities (4%) are the origin of all three types of mobility; these are often China’s most developed cities including Beijing, Shanghai, and Shenzhen. Only 273 cities act as destinations of patient mobility, among which 24 cities host only the mobility for better treatment/diagnosis. Two cities host only the pseudo-patient mobility, 15 cities (5%) host both the mobility for the best and for better treatment/diagnosis, while the other 232 cities (85%) host both the mobility for better treatment/diagnosis and pseudo-patient mobility. Notably, cities with over 50% of hosted mobility as the pseudo-patient mobility are mainly provinces in the middle of China, including Hubei, Hunan, and Jiangxi (Fig. 2c) where the amount of outflowing migrant workers is among the highest in China. Role of cities in inter-urban healthcare provision To discern the role of cities in inter-urban healthcare provision, we cluster the 273 cities into four types according to the size and type of patients they host, and other city attributes related to healthcare provision to non-local patients (see Methods). Type A cities exhibit the highest number of non-local patient origins (mean=235, p <1%, Fig. 3b), generating the largest catchment area and serving the highest number of non-local patients (mean=53,362, Fig. 3c). The 13 Type A cities provide 35% of all the inter-urban healthcare resources (Fig. 5), meeting the needs for the best (mean ratio=15%) and better (mean ratio=85%) treatment/diagnosis (Fig. 3g). Both the abundance in quality healthcare and high transport accessibility (Fig. 3d-e) determine Type A cities’ uppermost attractiveness to non-local patients. Hence, we refer to Type A cities as national healthcare hubs, as exemplified by Beijing, Shanghai, and Chengdu. Type B cities exhibit the second-highest diversification of non-local patient origin cities (mean=132). They serve a considerable but much lower number of non-local patients than do Type A cities (mean=20,387， p <1%). Having fewer quality healthcare services than Type A cities ( p <1%), T ype B cities attract a lower ratio of non-local patients from nonadjacent provinces (Supplementary Fig. 5a). Rather than serve a nationwide catchment area, Type B cities serve the nearby cities of their own and adjacent provinces and mainly meet the needs for better treatment/diagnosis (mean ratio=77%). Hence, we refer to Type B cities as regional healthcare hubs, as exemplified by Kunming, Shenyang, and Fuzhou. Type C cities exhibit the lowest number of non-local patients (mean=1,268) and the fewest patient origin cities (mean=18). With the least richness of quality healthcare, Type C cities attract few non-local patients, and the majority of the non-local patients they do serve are returning migrant workers (mean ratio=52%) from the province’s capital city. After excluding pseudo-patient mobility, the number of origin cities of non-local patients served by Type C cities is minimal (Supplementary Fig.4). Hence, we refer to Type C cities as pseudo- patient destinations . Type D cities differ from Type C cities by having a slightly higher number of non-local patients (mean=5,623, p <1%) and origin cities (mean=60, p <1%). They provide healthcare to meet the needs for better treatment/diagnosis (mean ratio=56%) and of returning migrant workers (mean ratio=44%). Unlike Type C cities, most of the returning patients are from a range of megacities instead of the single source of the province capital city (Supplementary Fig. 4c-d). After excluding pseudo-patient mobility, the number of non-local patients served by Type D cities is low. Hence, we refer to Type D cities as occasional destinations with a very limited function of serving non-local patients. Type O cities do not serve any non-local patients. The poorest transport accessibility and the lack of quality healthcare mean such cities are rarely a destination for non-local patients or for the home-returning patients, even if they have outflowing migrant workers. Type O cities are often remote cities near border regions (Fig. 3a). Figure 3a conceptualizes the four types of cities regarding the function of inter-urban healthcare provision. With substantial catchment areas constitutive of a wide range of cities, Type A and B cities have a real function of serving other cities that lack quality healthcare; they comprise 12% of Chinese cities (number=13 and 30 respectively). Comprising 63% of Chinese cities (number=141 and 89 respectively), Type C and D cities host mainly the returning migrants and occasional visits from non-local patients with few functions of serving nearby cities. Role of cities in inter-urban healthcare demand We use the same clustering method to discern the role of cities in inter-urban healthcare demand (see Methods), with four types of cities classified. Type 1 cities exhibit the highest number of outflowing patients (mean=33,097, p <1%, Supplementary Fig. 6a), and the patients go the farthest (mean=660km, Fig. 4c) to reach the greatest diversification of destinations for healthcare (mean=176, p <1%, Fig. 4b). On average, 59% of the patients go outside their province (Supplementary Fig. 8a). Type 1 cities are often the developed megacities, which are the richest in quality healthcare (Fig. 4e), and the populations’ education conditions and social networks are also the best, allowing the residents to know the best physicians and hospitals in other cities for treating their illnesses 10, 31 . After excluding pseudo mobility, almost half of their outflowing patients (mean ratio=49%) travel to obtain the best treatment/diagnosis (Supplementary Fig. 7). This suggests that the residents in Type 1 cities are mobile nationwide to obtain the best diagnosis/treatment . Type 2 cities exhibit the second highest number of outflowing patients, but this number is still much lower than for Type 1 cities (mean=12,610, p <1%). The same is true of the diversification of destinations (mean=103). On average, about 42% of the outflowing patients from Type 2 cities go outside their province to seek quality healthcare. Even though Type 2 cities are relatively rich in local healthcare (Fig. 4e), the majority of their outflowing patients pursue better treatment/diagnosis outwards (mean ratio=78%). Only one third of Type 2 cities have pseudo-patient outflows, as they are not the major host cities of migrant workers. Type 2 cities are often medium-sized cities in the developed eastern coastal region which have good transport accessibility and economic conditions, so their residents can travel to reach nearby healthcare-rich cities (Fig. 4d and Supplementary Fig. 6b). This suggests that the residents in Type 2 cities are mobile regionwide for better diagnosis/treatment. Type 3 cities exhibit a minimal number of outflowing patients (mean=1,858) and destinations (mean=16), and only 25% of the patients go outside their province (Supplementary Fig. 8a) to several cities in nearby provinces (mean=11). Type 3 cities are the most deprived of quality healthcare (Fig. 4e), while the worst transport accessibility and economic conditions limit the residents’ ability to travel far and wide to obtain healthcare. Type 3 cities are often small cities in the less-developed Western inland region (Fig. 4a). This indicates that the residents in Type 3 cities are immobile regarding obtaining better diagnosis/treatment. Type 4 cities differ from Type 3 cities by having a higher percentage of outflowing patients going beyond the province boundary (mean ratio=35%) and a higher number of destinations (mean=48, p <1%). The much better transport accessibility ( p<1% ) enables the residents of Type 4 cities to travel to more nearby healthcare-rich cities, although they suffer from poor economic conditions and a shortage of quality healthcare similar to Type 3 cities. They are often the small cities in central and coastal China. This indicates that the residents in Type 4 cities are mobile locally to obtain better diagnosis/treatment. Figure 4a conceptualizes the four types of cities regarding the function of inter-urban healthcare demand. Comprising only 4% of Chinese cities, Type 1 cities create 27% of the inter-urban healthcare demand, among which most is for the best diagnosis/treatment by their residents moving nationwide. Not rich in quality healthcare and comprising 9% of Chinese cities, Type 2 cities demand 20% of the inter-urban healthcare resources by their residents travelling regionwide for better diagnosis/treatment. The remaining 87% of cities are Type 3 and 4 cities, which demand 53% of the inter-urban healthcare resources (Fig. 5). Inter-dependent function and the benefit structure The patient flows among different types of cities reflect the inter-dependency between healthcare providers and healthcare demanders, with flow size indicating intensity of interaction (Fig. 5). National hub cities serve Type 4 cities the most (15%) rather than the most disadvantaged Type 3 cities, indicating that accessibility to healthcare-rich cities offsets the local shortage of healthcare. Meanwhile, regional hub cities provide healthcare substantially to both Type 3 and 4 cities. The advantaged Type 1 cities depend substantially on occasional destination cities to provide healthcare to their residents (9%). One possible reason is that Type 1 cities, mostly China’s first-tier megacities, have a concentration of jobs involving frequent travelling 32 . Therefore, they generate healthcare demands during business trips to a wide range of cities 33-34 . The unnoticed pseudo-destination cities, notably, function to serve the advantaged Type 1 and 2 cities, which host the majority of China’s migrant workers. We discern the benefit structure of the IHS by examining the dual roles of cities. Combining the provision and demand roles yields nine compound modes involving more than three cities (Fig. 6a). For each city, we consider (1) the sufficiency of the local healthcare provision for the local population, and (2) the ability to obtain inter-urban healthcare provision for the local population. As a city both gains and loses healthcare resources within the IHS, we measure each city’s relative gains of inter-urban healthcare resources (see Methods). The results indicate significant differences in the two attributes among different compound modes (Fig. 6b-c). Accordingly, we infer which compound modes benefit from the IHS regarding ensuring healthcare provision to the local population. Referring to Figure 6a, cities at the junction of Type A and Type 1 are labelled as magnates because they leverage the nationwide IHS to supplement local healthcare provision, which is already the best. Accordingly, cities at the junction of Type B and Type 2 are local magnates . They benefit less from the IHS and depend mainly on a regional base. We describe cities at the junction of Type D and Type 2 as free-riders in that they do not contribute local resources to the IHS while obtaining inter-urban resources to compensate for the local shortage of quality healthcare. We refer to cities at the junction of Type C and Type 4 as traders because they offer local resources to remediate the megacities’ supply deficiency for meeting the healthcare needs of migrant workers while obtaining inter-urban resources to serve local residents. They thus exchange benefits with other cities in the IHS. Similarly, cities at the junction of Type D and Type 4 are also traders that exchange their service function of meeting occasional healthcare demand from megacities for the supply to their local populations. In contrast, refuges , which refers to cities at the junction of Type C and Type 3, can scarcely benefit from the IHS; they do not obtain inter-urban resources to offset their shortage of supply to the local population, while providing substantially local resources to returning migrant workers. At the junction of Type D and Type 3 , caretakers are also disadvantaged in the IHS. They do not obtain additional supply from other cities to serve local populations while providing the scarce local resources to meet the occasional healthcare demand from megacities. Givers are cities at the junction of Type B and Type 4 , which share self-sufficient local resources with nearby cities but obtain few resources from the IHS. In total, magnates, local magnates, and free-riders constitute the winners of the IHS, respectively comprising 4%, 4%, and 5% of Chinese cities and accommodating 16%, 8% and 8% of the Chinese population. On the other side are refuges , caretakers , and givers , which respectively comprise 24%, 7%, and 4% of Chinese cities and accommodate 13%, 6%, and 6% of the Chinese population. Traders (27%) are in an ambiguous position in the benefit structure. Interestingly, a majority (24%) of the cities are at the junction of Type O and Type 3; they accommodate 9% of the population of China and are outsiders , as they demand little from the IHS and provide nothing in return. The results point towards a Matthew-effect of accumulated advantage. The cities rich in local healthcare provision get richer through the IHS. Discussion Studies on patient mobility has informed policymaking on how to improve healthcare efficiency and equity. However, the IHS behind the growing and diverse patient mobility remains largely unknown. This paper addresses the knowledge gap by utilizing human mobility big data to establish a typology of roles of Chinese cities based on their functions in providing/demanding inter-urban healthcare resources. Moreover, it unveils the benefit structure of the IHS, emphasizing the imbalanced inter-urban dependence. The study draws three significant conclusions. First, the interweaving of three types of patient mobility in cities shapes the IHS. The pseudo-patient mobility by migrant workers is highlighted which adds complexity to the role of cities in providing and demanding healthcare. The fast yet unequal urbanization in China and other developing countries 8-12 , which involves massive labor flows into a few megacities and the institutional exclusion of migrant workers from the urban welfare system, poses significant challenges to ensuring equal rights to adequate healthcare. Pseudo-patient mobility not only reflects the unfair urbanization but also highlights the unjust exploitation of healthcare resources in less developed cities to sustain the economy and labor regime of developed megacities. Second, cities’ roles show distinct disparities regarding inter-urban healthcare provision and demand. A small proportion (8%) of cities, specifically national and regional hubs, play a role in addressing the healthcare shortage in other cities, while a majority (63%) of cities compensate for the healthcare rights migrant workers are denied in megacities. However, China's current national healthcare reform focuses primarily on strengthening the function of regional hub cities, thus overlooking the significance of pseudo-destination cities in promoting equity in healthcare access. The study suggests that the unnoticed pseudo-destination cities should receive attention and investments to enhance their function. This is crucial considering the uneven urbanization pattern in China and the institutionalized spatial mismatch between work and welfare provision for migrant workers. Third, cities are involved unequally in the IHS. The IHS exhibits a Matthew effect, where a few already advantaged cities benefit more and can strengthen their structural advantages in meeting the healthcare demand of the local population. In contrast, the majority of cities, particularly those facing healthcare shortages, are further disadvantaged in ensuring adequate healthcare for their local populations. While the IHS offers the potential for compensating and supplementing the local healthcare provision, the already advantaged cities take greater advantage of this opportunity, especially in relation to providing healthcare for migrant workers. Accordingly, the IHS generates new structural inequalities in healthcare access while addressing the spatial mismatch between healthcare provision and demand. Policies aimed at enhancing healthcare access equity should consider improving the benefit structure of the system toward more balanced configurations. This includes transforming the position of disadvantaged cities, including refuges , caretakers , and givers , to more advantaged ones, like traders . While providing a new perspective of the urban system to tackle equity in healthcare access, this study enhances our understanding of the vertical (function beyond job creation and economic agglomeration) and the horizontal (inter-dependent function between cities) extension of cities' roles in urban systems. Manuel Castells 35 introduced the concept of space of flows to redefine a place as a nexus of flows of people, capital, goods, and information. Cities develop inter-dependent functions upon these flows 36 . Previous studies have focused on how inter-urban economic flows interweave to form an urban system and to shape the global cities as centers of economic command and control 19, 21 . However, our study reveals that cities also extend their functions to provide or demand healthcare mutually alongside the increasing mobility of populations seeking healthcare. The study highlights the interconnectedness of healthcare provision and demand within the urban system, emphasizing the importance of understanding cities' extended roles beyond economic functions. Methods Data on human mobility Cellphones communicate with transceivers regularly, and the communication log can be used to decipher the locations and moving trajectories of users at a full temporal coverage 37 . In this study, we used a set of cellphone-user trajectory data collected between 1 st and 31 st December 2022 from China Unicom, which is one of China’s top three telecom companies whose cellphone users comprise about 20% of the market share in China 38 . The data cover over 300 million cellphone users in China during the study period. Data on location and areas of hospitals In China, patients travel mainly for quality healthcare rather than basic healthcare 12, 39-40 . We thus include the non-local patients’ visits to the top hospitals in the analysis. In China, Tertiary-A hospitals are officially recognized by the Chinese government as the hospitals offering the highest quality healthcare 12 . Hence, in this study, patient mobility is calculated as a patient’s movement to utilize the healthcare of a Tertiary-A hospital located in a city other than the patient’s city of residence. A list of Tertiary-A hospitals was provided by the National Health Commission of the People’s Republic of China in 2022, which is the highest authority in charge of China’s national healthcare system. In total, 1,404 Tertiary-A hospitals were included. We searched the location of each Tertiary-A hospital on Baidu map (https://map.baidu.com/) and collected the area-of-interest (AOI) data of each Tertiary-A hospital using a web crawler. Data on locational attributes of cities The analyses of this study involve the data on cities’ attributes related to cross-city healthcare utilization. For local healthcare provision level, the number of hospital beds is widely used as a measure 9, 12 , and we collected the data from official website of each hospital. We measured local economic development level by the annual GDP of 2022 and collected the data from China Statistical Yearbook (https://www.stats.gov.cn). We measured local transportation accessibility by the degree centrality of cities in national railway networks and collected the data from the online ticketing system of China's railways administration (https://www.12306.cn/index/). Patient mobility identification We used a three-step analysis (see Supplementary Fig. 13) to identify patient mobility from the trajectory data and AOI data: (1) a residence city is calculated as the city in which a cellphone user had the longest stop during nighttime (from 9 p.m. to 8 a.m.) over the previous 12 months; (2) a trans-city healthcare utilization is calculated as a cellphone user’s visit to the area of a Tertiary-A hospital not in the residence city, and the stay in the area of the Tertiary-A hospital was over 30 minutes; and (3) a non-local patient is defined as a cellphone user who visited and stayed in the area of a Tertiary-A hospital not in the residence city for over 30 minutes. Finally, we identified almost 19 million (18,993,351) visits to 1,404 Tertiary-A hospitals in China from over 300 million cell phone users’ daily movements generated in December 2022, among which 1,984,448 were trans-city healthcare utilizations made by non-local patients. Classifying patient mobility based on the purpose According to the literature, there are three main types of patient mobility in China, and we used a three-step method to identify them (Supplementary Fig. 14). The first follows the classical pull-push theory of amenity-driven migration 41 whereby a patient travels to another city for better treatment/diagnosis. Accordingly, we calculate the patient mobility from the residence city to a city richer in quality healthcare as the mobility for better treatment/diagnosis . The second is when a patient travels to another city for the best treatment/diagnosis, which is common especially among the wealthy populations 6 . We thus calculate the patient mobility from a residence city that is the richest in quality healthcare to another city that is also the richest in quality healthcare as the mobility for the best treatment/diagnosis . The third is when a migrant worker returns from the city where they work and reside to their home city for healthcare, and the home city is less rich in healthcare resources than the host city, which is inconsistent with the push-pull theory 8 . This emerged especially in developing countries that have fast and uneven urbanization. China’s urbanization since the 1990s was characterized by the inflow of massive populations from villages, towns, and small cities to megacities for job opportunities 42 , while the migrant workers have largely been excluded from the megacities’ social welfare system including the healthcare insurance system 26 . Thus, migrant workers return to the home city for healthcare during holidays and work leave 27 . The 4 first-tier cities (i.e., Beijing, Shanghai, Shenzhen, and Guangzhou), another 2 municipalities directly under the central government (i.e., Tianjin and Chongqing), and 22 provincial capital cities (excluding Haikou, Yinchuan, Lhasa and Hohhot), where over 110 million of the migrant workers are concentrated, host over 30% of all the floating populations in China 43 . They are also rich in quality healthcare (Supplementary Fig. 15) and have the strict hukou -based welfare system that excludes migrant workers. We thus calculate the mobility of patients who reside in the 28 major cities to the cities which are prominently deprived of quality healthcare as the pseudo-patient mobility . A city’s lack/richness of quality healthcare is measured by the capacity of all the Tertiary-A hospitals of the city, and the capacity of a Tertiary-A hospital is measured by the number of hospital beds. We use the Jenks natural breaks algorithm to determine the threshold value of the level of capacity of quality healthcare. The cities in the category of the highest level (level 1) of Tertiary-A hospital capacity are defined as being the richest in quality healthcare, and the cities in the last two levels (3 and 4) are defined as being significantly deprived regarding quality healthcare and are recognized as the destination of pseudo-patient mobility from the 28 major cities (Supplementary Fig. 15). Clustering cities based on the dual roles in trans-city healthcare utilization We cluster all the 364 Chinese cities according to their role as the origin (demand) and destination (provision) of patient mobility, respectively. All 364 cities had an outflow of patients, and they are all included in the clustering analysis of origin cities. However, only 273 cities had an inflow of non-local patients, and they are thus included in the clustering analysis of destination cities. The remaining 91 cities hosting no non-local patients are placed into one group manually. For clustering destination cities, we consider three dimensions of the seven attributes that depict a city’s capacity for serving non-local patients (Supplementary Table 1): (1) scope of services, including (1a) vertical scope regarding the geographical depth of catchment areas, which is measured by the weighted average distance between the destination city and the origin cities of the served non-local patients, and (1b) horizontal scope regarding the diversity of origin cities, which is measured by the number of origin cities of the served non-local patients; (2) necessity of services, including (2a) healthcare deprivation of served origin cities, which is measured by the weighted number of Tertiary-A hospital beds per 1,000 persons of origin cities of the served non-local patients; and (2b) economic level of served origin cities, which is measured by the weighted GDP per capita of origin cities of the served non-local patients; (3) type of mobility of the inflowing patients, which is measured by percentage of mobility for the best, for better diagnosis/treatment, and pseudo-patient mobility, respectively For clustering origin cities, we consider four dimensions of the seven attributes that depict the ability and motivation of a city’s residents to move to access non-local quality healthcare (Supplementary Table 2): (1) scope of mobility including (1a) vertical scope regarding the geographical depth of destination cities which is measured by weighted average distance between the origin city and the destination cities of outflowing non-local patients, and (1b) horizontal scope regarding the diversity of destination cities, which is measured by the number of destinations of outflowing non-local patients; (2) motivation to move regarding the deprivation of the local quality healthcare, which is measured by the number of Tertiary-A hospital beds per 1,000 persons; (3) economic ability to move, which is measured by GDP per capita of the city; and (4) type of mobility of the outflowing patients, which is measured by percentage of mobility for the best, for better diagnosis/treatment, and pseudo-patient mobility, respectively The bisecting K-Means is used for clustering cities. This is an unsupervised clustering algorithm that is based on the traditional K-Means method but is deemed a more efficient and more accurate technique 44 . As a divisive hierarchical clustering algorithm, the bisecting K-Means assigns all data points to one cluster at first, and the K-Means is repeatedly applied to the parent cluster, which is known as ‘C’ in order to obtain the best subclusters, which are then known as ‘C1’ and ‘C2’ 45 . The formal procedure of the bisecting K-Means is as follows: (1) assigning a parent cluster C to be split; (2) using K-Means to split C into subclusters C1 and C2; (3) calculating the inter-cluster similarity (e.g., Euclidean distance) for C1 and C2 until a fixed number of iterations is achieved; (4) selecting the subclusters with the highest inter-cluster dissimilarity (e.g., biggest sum of squared errors); and (5) repeating steps (2) and (3) until k clusters are formed. The elbow index (sum of squared errors) and the Silhouette Coefficient are used in the bisecting K-Means to determine the number of clusters, and the best cluster number is 4 both for the origin and for the destination cities (Supplementary Fig. 16). Measuring relative gains in inter-urban healthcare resources Here, we assume that the IHS operates similarly to the trade system where a city both imports and exports healthcare resources. The patient outflows and inflows indicate the volume of import and export. Hence, we introduce the concept of Vollrath’s revealed comparative advantage 46 to construct the relative import advantage index (Formula 1) to measure a city’s relative advantage in obtaining inter-urban healthcare resources, and the relative export advantage index (Formula 2) to measure a city’s relative advantage in providing inter-urban healthcare resources. Finally, we use the ratio of relative import advantage to relative export advantage (Formula 3) to measure a city’s relative gains in IHS. The larger the value, the more a city gains from IHS. Declarations Acknowledgements We gratefully acknowledge the financial support for this research provided by National Natural Science Foundation of China (41925003, 42301220)， Shenzhen science and technology program (JCYJ20220818100810024, KQTD20221101093604016, RCBS20221008093306009); Guangdong Basic and Applied Basic Research Foundation (2023A1515010875); Humanities and Social Sciences Fund of the Ministry of Education of China (Grant Number: 23YJC840037). Author contributions J.L. contributed to conceptualization, methodology, data curation, formal analysis, visualization, result interpretation, writing-original draft, writing-review and editing, and funding acquisition. P.Z. contributed to writing-review and editing, supervision, and funding acquisition. 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The supervision of trans-province healthcare utilization is faced with four problems. How to improve supervision efficiency? https://new.qq.com/rain/a/20230818A085CT00 National Health Commission of the People’s Republic of China. (2022a). Statistical bulletin on China’s health care development 2022 . Gosnell, H., & Abrams, J. (2011). Amenity migration: diverse conceptualizations of drivers, socioeconomic dimensions, and emerging challenges. GeoJournal , 76 , 303-322. Guan, X., Wei, H., Lu, S., Dai, Q., & Su, H. (2018). Assessment on the urbanization strategy in China: Achievements, challenges and reflections. Habitat International , 71 , 97-109. National Bureau of Statistics. (2021). The seventh national census . Savaresi, S. M., & Boley, D. L. (2001, April). On the performance of bisecting K-means and PDDP. In Proceedings of the 2001 SIAM International Conference on Data Mining (pp. 1-14). Society for Industrial and Applied Mathematics. Banerjee, S., Choudhary, A., & Pal, S. (2015, December). Empirical evaluation of k-means, bisecting k-means, fuzzy c-means and genetic k-means clustering algorithms. In 2015 IEEE international WIE conference on electrical and computer engineering (WIECON-ECE) (pp. 168-172). IEEE. Vollrath, T.L. (1991). A theoretical evaluation of alternative trade intensity measures of revealed comparative advantage. Weltwirtschaftliches Archiv 127 , 265–280. Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2025 Read the published version in Nature Cities → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4837017","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":342818827,"identity":"2fda5189-79c1-40fc-b82d-06fd7aaa0e74","order_by":0,"name":"Pengjun Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYLACCRjxgSEBxDQgXgvjDKK1wPQx8xCjxZy99/ALy7Y7DPKzmx8+tt2RltjA3rxNgqHmDk4tlj3n0iwk254xMM45ZmyceyYnsYHnWJkEw7FnOLUY3MgxM5BsO8zALJFgJp3bVpHYIJFjJsHYcBi3lvtvIFrYJNK/SVuCtMi/IaDlBo/xA5AWHqDh0oxtQIdJ8ODXYtmTY8Ygce4wj4RETrFh75k04zaetGKLhGO4tZiznzH+LFF2WE5+RvrGBz93JMv2sx/eeONDDR6HMTCwSQOjhAfMY2wAckGMBJwawFqYP36A8UBaRsEoGAWjYBSgAwAV61CXLIvS+wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5373-5551","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Pengjun","middleName":"","lastName":"Zhao","suffix":""},{"id":342818828,"identity":"1e04cfbb-4ecc-4262-909b-7d539440a5c9","order_by":1,"name":"Juan Li","email":"","orcid":"","institution":"School of Urban Planning and Design, Peking University Shenzhen Graduate School","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Li","suffix":""},{"id":342818829,"identity":"b48f7361-7e3a-4b89-8bbe-2a5e5fb2c059","order_by":2,"name":"Mengzhu Zhang","email":"","orcid":"","institution":"School of Urban Planning and Design, Peking University Shenzhen Graduate School","correspondingAuthor":false,"prefix":"","firstName":"Mengzhu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-07-31 16:20:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4837017/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4837017/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44284-024-00185-8","type":"published","date":"2025-01-02T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63128868,"identity":"871596bc-6790-4678-a8c8-dfc01a658c41","added_by":"auto","created_at":"2024-08-23 12:43:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1359814,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic illustration of how patient mobility results in an inter-urban healthcare system. a. The dichotomy of local and non-local patients. b. Three types of patient mobility with different purposes: “Best” refers to mobility for the best treatment/diagnosis, “Better” refers to mobility for better treatment/diagnosis, and “Pseudo” refers to pseudo-patient mobility. c. Urban system formed through the mobility of capital, goods, information, etc. d. The inter-urban healthcare system formed through three types of patient mobility.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4837017/v1/c419b2b8ddc3989f74f6a536.png"},{"id":63127935,"identity":"182e215e-d76e-431e-825e-045b45bdd655","added_by":"auto","created_at":"2024-08-23 12:35:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196246,"visible":true,"origin":"","legend":"\u003cp\u003eCities involved unequally in three types of patient mobility. a. Percentage of three types of patient mobility in total, and percentage of cities which have different types of patient mobility as origins and destinations. b. Percentage of pseudo-patient mobility in outflowing patients of origin cities. c. Percentage of pseudo-patient mobility in inflowing patients of destination cities.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4837017/v1/9d62f5dcca135d4d0adaa8e2.png"},{"id":63129671,"identity":"8804ecd7-6ae8-4ff2-ab8f-97728839e20c","added_by":"auto","created_at":"2024-08-23 12:59:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334804,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of four types of cities regarding inter-urban healthcare provision. a. Spatial distribution of four types of cities with \u003cem\u003eTypes A\u003c/em\u003e and \u003cem\u003eB\u003c/em\u003e mostly located in East China. The four conceptual graphs indicate the average volume of the three types of patient mobility served at different spatial scales. The angle of each fan with arrows refers to the average volume of inflowing non-local patients. The number in each circle refers to the average number of served cities at the corresponding spatial scale. b. Horizontal scope of services measured by number of served cities. c. Number of inflowing patients. d. Transport accessibility measured by in-degree centrality of cities in national railway networks. e. Capability of quality healthcare measured by number of Tertiary-A hospital beds per 1,000 persons. f. Relationship between number of served cities and number of inflowing patients. g. Average ratio of the three types of patient mobility in the four types of cities regarding healthcare provision (“t/d” in the figure refers to “treatment/diagnosis”). The Mann-Whitney test is applied to examine the statistical difference between different types; the lines with *** are significant at the 1% confidence level, with ** are significant at the 5% confidence level, and with * are significant at the 10% confidence level. Those with “n.s.” are “not significant”.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4837017/v1/6f1087830f5089452e9322b4.png"},{"id":63127930,"identity":"fa712caf-90fb-47b0-ae27-69dddc755a9d","added_by":"auto","created_at":"2024-08-23 12:35:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":316047,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of four types of cities regarding inter-urban healthcare demand. a. Spatial distribution of four types of cities with \u003cem\u003eType 1\u003c/em\u003eand \u003cem\u003e2\u003c/em\u003e mostly located in East China. The four conceptual graphs indicate the average volume of the three types of patient mobility demanded at different spatial scales. The angle of each fan with arrows refers to the average volume of outflowing non-local patients. The number in each circle refers to the average number of destination cities at the corresponding spatial scale. b. Horizontal scope of mobility measured by number of destination cities. c. Vertical scope of mobility measured by weighted travel distance of outflowing patients. d. Economic ability of cities to move measured by GDP per capita. e. Motivation to move measured by number of Tertiary-A hospital beds per 1,000 persons. f. Relationship between number of destination cities and number of outflowing patients. g. Average ratio of the three types of patient mobility in the four types of cities regarding healthcare demand (“t/d” in the figure refers to “treatment/diagnosis”). The Mann-Whitney test is applied to examine the statistical difference between different types.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4837017/v1/5b0531c64e82519c4fdc263f.png"},{"id":63129226,"identity":"f71b92ae-25e3-4247-8813-79599b29f9fb","added_by":"auto","created_at":"2024-08-23 12:51:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":129117,"visible":true,"origin":"","legend":"\u003cp\u003eInter-dependent healthcare provision/demand functions among cities. Number in brackets refer to the percentage of cross-city healthcare utilization of the corresponding type.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4837017/v1/a286e875370752c3f72d478d.png"},{"id":63127936,"identity":"ae49c8e7-1a70-4720-8ab2-95804feb5387","added_by":"auto","created_at":"2024-08-23 12:35:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":276488,"visible":true,"origin":"","legend":"\u003cp\u003eCompound modes and the benefit structure. a. Nine compound modes combining the provision and demand roles. Number in the brackets refers to the number of cities in the cross. b. The sufficiency of local healthcare provision to local population measured by the number of Tertiary-A hospital beds per 1,000 persons (left), and the ability to obtain inter-urban healthcare provision to local population measured by the relative gains of each mode under the IHS (right). The Mann-Whitney test is applied to examine the statistical difference between different modes. c. Relationship of each mode between the sufficiency of local healthcare provision and the ability to obtain inter-urban healthcare provision.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4837017/v1/d869ec61f692712cbfc41e5d.png"},{"id":72875657,"identity":"9e0b3d2d-3d78-4cda-9b35-1db19f8b7739","added_by":"auto","created_at":"2025-01-03 08:09:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3420521,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4837017/v1/9d1358e6-67c3-49bb-b82f-4437a106cfe6.pdf"},{"id":63127937,"identity":"b9139a4d-af7b-48ec-8223-945a2b2e878c","added_by":"auto","created_at":"2024-08-23 12:35:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30979443,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4837017/v1/35669795fe4e0cdf1c8061d9.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Unequal roles of cities in the inter-urban healthcare system","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCities are increasingly important in shaping people\u0026rsquo;s health outcomes\u003csup\u003e1\u003c/sup\u003e. Not only are they often the epicenters of disease origins and transmissions\u003csup\u003e2-3\u003c/sup\u003e,\u0026nbsp;but they are also crucial in delivering healthcare services\u003csup\u003e4\u003c/sup\u003e.\u0026nbsp;While cities have long been recognized as providers of healthcare to their local populations\u003csup\u003e4\u003c/sup\u003e, there is a growing trend of people seeking healthcare outside their city of residence\u003csup\u003e5-6\u003c/sup\u003e. Under the fast urbanization, the scarcity and uneven distribution of healthcare services, particularly high-quality ones, is common and cause the worldwide patient mobility between cities, regions and national jurisdictions\u0026nbsp;\u003csup\u003e5-7\u003c/sup\u003e,\u0026nbsp;as found in the US, Turkey, Italy, Iran, Laos, Thailand, India, and China\u003csup\u003e7-12\u003c/sup\u003e. Cities have thus developed the function of providing healthcare to non-local residents.\u0026nbsp;The resulting inter-dependence of healthcare provision/demand among cities indicates the formation of what we\u0026nbsp;term\u0026nbsp;an inter-urban healthcare system (IHS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe hidden IHS can be explored through\u0026nbsp;the lens of patient mobility\u003csup\u003e6-8\u003c/sup\u003e. Previous studies have examined\u0026nbsp;which groups are\u0026nbsp;moving to obtain non-local healthcare and why\u003csup\u003e6, 10\u003c/sup\u003e, and the size,\u0026nbsp;networks, and\u0026nbsp;temporal-geographical patterns of patient mobility\u003csup\u003e11-12\u003c/sup\u003e. Early studies\u0026nbsp;considered\u0026nbsp;patient mobility to be detrimental to healthcare access equity by exposing social disparities in the ability to travel for medical care\u0026nbsp;\u003csup\u003e5-6\u003c/sup\u003e, and the underlying multiple purposes, which range from the wealthy population\u0026rsquo;s desire for the best treatment/diagnosis to the deprived populations\u0026rsquo; needs for\u0026nbsp;better or even\u0026nbsp;basic ones\u003csup\u003e6\u003c/sup\u003e. Recent studies have pivoted to understand patient mobility as conducive to the improvement of healthcare efficiency and equity, especially repairing the spatial mismatch of healthcare\u0026nbsp;provision and demand\u003csup\u003e7\u003c/sup\u003e. This shift guides the current policy framework towards encouraging patient mobility. For instance,\u0026nbsp;the EU launched a Directive to secure patients\u0026rsquo; rights to utilize healthcare in other EU countries\u003csup\u003e13\u003c/sup\u003e; China launched national reforms to remove the institutional barriers to cross-city healthcare utilization\u0026nbsp;and\u0026nbsp;announced\u0026nbsp;a regional healthcare center plan\u0026nbsp;to\u0026nbsp;encourage patients\u0026rsquo; trips to nearby hub cities with adequate healthcare\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eNevertheless,\u0026nbsp;little is known about\u0026nbsp;how\u0026nbsp;patient mobility\u0026nbsp;with different purposes\u0026nbsp;forms an\u0026nbsp;IHS\u0026nbsp;and the role of cities in it. Three questions remain to be answered: (1) How can the features of an IHS be identified through patient mobility? (2) What roles do the cities play in the IHS? and (3) How\u0026nbsp;unequal are the roles of cities regarding\u0026nbsp;healthcare access within the IHS?\u003c/p\u003e\n\u003cp\u003eLeveraging the emerging human mobility big data\u003csup\u003e15-17\u003c/sup\u003e, this study answers the research questions and reveals the IHS in China. Three interrelated analyses constitute our inquiry. First, we identify nearly 2 million non-local patients\u0026rsquo; visits to\u0026nbsp;1,404\u0026nbsp;first-class hospitals from over 300 million cellphone users\u0026rsquo; daily movements in China, and we discern three types of patient mobility according to the purpose, especially including the pseudo-patient\u0026nbsp;mobility, which\u0026nbsp;refers\u0026nbsp;to the temporary return of migrant workers from megacities to healthcare-deprived\u0026nbsp;home cities for treatment/diagnosis. Second, based on a full picture of nationwide patient mobility, we classify the\u0026nbsp;roles\u0026nbsp;of Chinese cities in providing and demanding healthcare based mainly on the number and type of patients they host and lose. Third, we examine the inter-dependence among cities regarding healthcare provision/demand and unveil the hidden benefit structure\u0026nbsp;regarding\u0026nbsp;how cities\u0026nbsp;exploit the IHS to offset/strengthen their structural (dis)advantages in local healthcare provision.\u003c/p\u003e\n\u003cp\u003eThe significance of this study is threefold (Fig. 1).\u0026nbsp;First, we harness the potential of human mobility big data to\u0026nbsp;discover the features of IHS\u0026nbsp;by delivering a full picture of nationwide\u0026nbsp;patient mobility, overcoming the selection bias of current studies that use\u0026nbsp;incomplete\u0026nbsp;healthcare records and questionnaire surveys\u003csup\u003e11, 18\u003c/sup\u003e. Second, we offer insights into the horizontal diversification of cities\u0026rsquo; roles in urban systems under fast population mobility, \u003cem\u003ei.e.,\u0026nbsp;\u003c/em\u003einter-urban healthcare provision \u003cem\u003eversus\u003c/em\u003e demand functions. It thus extends the current focus of urban system studies beyond focusing solely on economic interdependence and hierarchy\u003csup\u003e19-21\u003c/sup\u003e. Third, we deepen the understanding of healthcare access equity by\u0026nbsp;combining the inequality of cities\u0026rsquo; role in the IHS\u0026nbsp;which provides valuable implications for planning\u0026nbsp;healthcare resources, while previous studies have addressed the issue mainly from individual affordability, accessibility, capability, hospital locations and community contexts\u003csup\u003e22-25\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThree types of patient mobility interweave to form an inter-urban healthcare system\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe proposed\u0026nbsp;a method to more fully identify cross-city\u0026nbsp;healthcare utilization\u0026nbsp;(see Methods) and found that three types of patient mobility interweave to form an IHS (Fig.2a). Primarily, mobility for better treatment/diagnosis accounts for 76% of all cross-city healthcare utilization, and only 5% are for the best treatment/diagnosis. The\u0026nbsp;pseudo mobility of migrant workers accounts for 19%;\u0026nbsp;they\u0026nbsp;return to their healthcare-deprived home cities for treatment/diagnosis due to being excluded from\u0026nbsp;the\u0026nbsp;healthcare welfare system in the megacities\u003csup\u003e26\u003c/sup\u003e or due to lacking\u0026nbsp;family support\u0026nbsp;during the treatment\u003csup\u003e27\u003c/sup\u003e. In China, the \u003cem\u003ehukou\u0026nbsp;\u003c/em\u003e(household registration) system denies migrant workers full rights to local welfare\u003csup\u003e28-29\u003c/sup\u003e, and over\u0026nbsp;380\u0026nbsp;million of the population are migrant workers who often live in the megacities separately from their families\u003csup\u003e30\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCities are not equally involved in the IHS\u0026nbsp;as the origins and destinations of patient mobility (Fig.2a); 92% of\u0026nbsp;364\u0026nbsp;Chinese cities (336) are the origin only of the mobility for better treatment/diagnosis, while 13 cities are the origin both of the mobility for better treatment/diagnosis and pseudo mobility. Only 15 cities (4%) are the origin of all three types of mobility; these are often China\u0026rsquo;s most developed cities including Beijing, Shanghai, and Shenzhen.\u003c/p\u003e\n\u003cp\u003eOnly 273 cities act as destinations of patient mobility, among which 24 cities host only the mobility for better treatment/diagnosis. Two cities host only the pseudo-patient mobility, 15 cities (5%) host both the mobility for the best and for better treatment/diagnosis, while the other 232 cities (85%) host both the mobility for better treatment/diagnosis and pseudo-patient mobility. Notably, cities with over 50% of hosted mobility as the pseudo-patient mobility are mainly provinces in the middle of China, including Hubei, Hunan, and Jiangxi (Fig. 2c) where the amount of outflowing migrant workers is among the highest in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of cities in inter-urban healthcare provision\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo discern the role of cities in inter-urban healthcare provision, we cluster the\u0026nbsp;273\u0026nbsp;cities into four types according to the size and type of patients they host, and other city attributes related to healthcare provision to non-local patients\u0026nbsp;(see Methods). \u003cstrong\u003e\u003cem\u003eType A\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ecities exhibit the highest number of non-local patient origins (mean=235,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt;1%,\u0026nbsp;Fig. 3b), generating the largest catchment area and serving the highest number of non-local patients\u0026nbsp;(mean=53,362,\u0026nbsp;Fig.\u0026nbsp;3c).\u0026nbsp;The 13\u0026nbsp;\u003cem\u003eType A\u0026nbsp;\u003c/em\u003ecities provide 35% of all the inter-urban healthcare resources\u0026nbsp;(Fig.\u0026nbsp;5), meeting the needs for the best (mean ratio=15%) and better (mean ratio=85%) treatment/diagnosis (Fig. 3g). Both the abundance in quality healthcare\u0026nbsp;and high transport accessibility\u0026nbsp;(Fig.\u0026nbsp;3d-e)\u0026nbsp;determine\u0026nbsp;\u003cem\u003eType A\u003c/em\u003e cities\u0026rsquo; uppermost attractiveness to non-local patients. Hence, we refer to\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eType A\u003c/em\u003e cities as\u0026nbsp;\u003cstrong\u003enational healthcare hubs,\u0026nbsp;\u003c/strong\u003eas\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eexemplified by Beijing, Shanghai, and Chengdu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eType B\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ecities exhibit the second-highest diversification of non-local patient origin cities (mean=132). They serve a considerable but much lower number of non-local patients than do\u0026nbsp;\u003cem\u003eType A\u0026nbsp;\u003c/em\u003ecities\u0026nbsp;(mean=20,387，\u003cem\u003ep\u003c/em\u003e \u0026lt;1%). Having fewer quality healthcare services than\u003cem\u003e\u0026nbsp;Type A\u0026nbsp;\u003c/em\u003ecities\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e \u0026lt;1%),\u0026nbsp;\u003cem\u003eT\u003c/em\u003e\u003cem\u003eype B\u003c/em\u003e cities attract a lower ratio of non-local patients from nonadjacent provinces\u0026nbsp;(Supplementary\u0026nbsp;Fig. 5a). Rather than serve a nationwide catchment area,\u0026nbsp;\u003cem\u003eType B\u003c/em\u003e cities serve the nearby cities of their own and adjacent provinces\u0026nbsp;and mainly meet the needs for better treatment/diagnosis (mean ratio=77%). Hence, we refer to\u0026nbsp;\u003cem\u003eType B\u003c/em\u003e cities as\u0026nbsp;\u003cstrong\u003eregional healthcare hubs,\u0026nbsp;\u003c/strong\u003eas exemplified by Kunming, Shenyang, and Fuzhou.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eType C\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ecities exhibit the lowest number of non-local patients (mean=1,268) and the fewest patient origin cities (mean=18). With the least richness of quality healthcare,\u0026nbsp;\u003cem\u003eType C\u003c/em\u003e cities attract few non-local patients, and the majority of the non-local patients they do serve are returning migrant workers\u0026nbsp;(mean\u0026nbsp;ratio=52%) from the province\u0026rsquo;s capital city.\u0026nbsp;After excluding\u0026nbsp;pseudo-patient mobility, the number of origin cities of non-local patients served by\u0026nbsp;\u003cem\u003eType C\u0026nbsp;\u003c/em\u003ecities is minimal\u0026nbsp;(Supplementary Fig.4). Hence, we refer to\u0026nbsp;\u003cem\u003eType C\u003c/em\u003e cities as\u0026nbsp;\u003cstrong\u003epseudo-\u003c/strong\u003e\u003cstrong\u003epatient\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edestinations\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eType D\u003c/em\u003e\u003c/strong\u003e cities differ from\u003cem\u003e\u0026nbsp;Type C\u003c/em\u003e cities by having a slightly higher number of non-local patients\u0026nbsp;(mean=5,623,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt;1%)\u0026nbsp;and origin cities (mean=60,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt;1%).\u0026nbsp;They provide healthcare to meet the needs for better treatment/diagnosis (mean ratio=56%) and of returning migrant workers\u0026nbsp;(mean\u0026nbsp;ratio=44%). Unlike\u0026nbsp;\u003cem\u003eType C\u0026nbsp;\u003c/em\u003ecities, most of the returning patients are from a range of megacities instead of the single source of the province capital city\u0026nbsp;(Supplementary Fig. 4c-d). After excluding\u0026nbsp;pseudo-patient mobility, the number of non-local patients\u0026nbsp;served by\u0026nbsp;\u003cem\u003eType D\u003c/em\u003e cities is low. Hence, we refer to\u0026nbsp;\u003cem\u003eType D\u003c/em\u003e cities as\u0026nbsp;\u003cstrong\u003eoccasional destinations\u003c/strong\u003e with a very limited function of serving non-local patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eType O\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ecities\u0026nbsp;do not serve any non-local patients. The poorest transport accessibility and the lack of quality healthcare\u0026nbsp;mean such cities are rarely a destination for non-local patients or for the home-returning patients, even if they have outflowing migrant workers.\u0026nbsp;\u003cem\u003eType O\u003c/em\u003e cities are often remote cities near border regions\u0026nbsp;(Fig. 3a).\u003c/p\u003e\n\u003cp\u003eFigure 3a conceptualizes the four types of cities regarding the function of inter-urban healthcare provision. With substantial catchment areas constitutive of a wide range of cities, \u003cstrong\u003e\u003cem\u003eType A\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ecities have a real function of serving other cities that lack quality healthcare; they comprise 12% of Chinese cities (number=13 and 30 respectively). Comprising 63% of Chinese cities (number=141 and 89 respectively), \u003cstrong\u003e\u003cem\u003eType C\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003eD\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ecities host mainly the returning migrants and occasional visits from non-local patients with few functions of serving nearby cities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of cities in inter-urban healthcare demand\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe use the same clustering method to discern the role of cities in inter-urban healthcare demand (see Methods), with four types of cities classified. \u003cstrong\u003e\u003cem\u003eType 1\u003c/em\u003e\u0026nbsp;\u003c/strong\u003ecities exhibit the highest number of outflowing patients\u0026nbsp;(mean=33,097,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt;1%,\u0026nbsp;Supplementary Fig. 6a),\u0026nbsp;and\u0026nbsp;the patients go the farthest (mean=660km, Fig. 4c)\u0026nbsp;to reach the greatest diversification of destinations for healthcare (mean=176,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt;1%,\u0026nbsp;Fig. 4b). On\u0026nbsp;average, 59% of the patients go outside their province (Supplementary Fig. 8a). \u003cem\u003eType 1\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ecities are often the developed megacities,\u0026nbsp;which are the richest in quality healthcare\u0026nbsp;(Fig. 4e),\u0026nbsp;and\u0026nbsp;the populations\u0026rsquo; education conditions and social networks are also the best, allowing the residents to know the best physicians and hospitals in other cities for treating their illnesses\u003csup\u003e10, 31\u003c/sup\u003e.\u0026nbsp;After excluding\u0026nbsp;pseudo mobility,\u0026nbsp;almost half of their outflowing patients (mean ratio=49%)\u0026nbsp;travel to obtain\u0026nbsp;the best treatment/diagnosis\u0026nbsp;(Supplementary Fig. 7). This suggests that the residents in \u003cem\u003eType 1\u003c/em\u003e cities are\u0026nbsp;\u003cstrong\u003emobile nationwide to obtain the best diagnosis/treatment\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eType 2\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ecities exhibit the second highest number of outflowing patients, but this number is still much lower than for \u003cem\u003eType 1\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ecities\u0026nbsp;(mean=12,610,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt;1%). The\u0026nbsp;same is true of the diversification of destinations (mean=103). On average, about 42% of the outflowing patients from\u003cem\u003e\u0026nbsp;Type 2\u003c/em\u003e cities go outside their province to seek quality healthcare. Even though \u003cem\u003eType 2\u003c/em\u003e cities are\u0026nbsp;relatively rich in local healthcare (Fig. 4e), the majority of their outflowing patients pursue better treatment/diagnosis outwards (mean ratio=78%).\u0026nbsp;Only one third of\u0026nbsp;\u003cem\u003eType 2\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ecities have pseudo-patient outflows,\u0026nbsp;as they are not the major host\u0026nbsp;cities\u0026nbsp;of migrant workers. \u003cem\u003eType 2\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ecities are often medium-sized cities in the developed eastern coastal region which have good transport accessibility and economic\u0026nbsp;conditions, so their residents can\u0026nbsp;travel\u0026nbsp;to reach nearby healthcare-rich cities\u0026nbsp;(Fig. 4d\u0026nbsp;and\u0026nbsp;Supplementary Fig. 6b). This suggests that the residents in \u003cem\u003eType 2\u003c/em\u003e cities\u0026nbsp;are \u003cstrong\u003emobile regionwide for better diagnosis/treatment.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eType 3\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ecities exhibit a minimal number of outflowing patients\u0026nbsp;(mean=1,858)\u0026nbsp;and destinations (mean=16), and only 25% of the patients go outside their province (Supplementary Fig. 8a)\u0026nbsp;to several cities in nearby provinces (mean=11). \u003cem\u003eType 3\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ecities\u0026nbsp;are the most deprived of quality healthcare\u0026nbsp;(Fig. 4e),\u0026nbsp;while the worst transport accessibility and economic conditions\u0026nbsp;limit the residents\u0026rsquo; ability to travel far and wide to obtain healthcare. \u003cem\u003eType 3\u003c/em\u003e cities are often small cities in the less-developed Western inland region\u0026nbsp;(Fig. 4a). This indicates that the residents in \u003cem\u003eType 3\u003c/em\u003e cities are\u0026nbsp;\u003cstrong\u003eimmobile regarding obtaining better diagnosis/treatment.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eType 4\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ecities differ from \u003cem\u003eType 3\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ecities by having a higher\u0026nbsp;percentage\u0026nbsp;of outflowing patients going beyond the province boundary (mean ratio=35%) and a higher number of destinations\u0026nbsp;(mean=48,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt;1%). The much better transport accessibility\u0026nbsp;(\u003cem\u003ep\u0026lt;1%\u003c/em\u003e)\u0026nbsp;enables the residents of \u003cem\u003eType 4\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ecities to travel to more nearby healthcare-rich cities, although they suffer from poor economic conditions and a shortage of quality healthcare similar to \u003cem\u003eType 3\u0026nbsp;\u003c/em\u003ecities. They are often the small cities in central and coastal China. This indicates that the residents in \u003cem\u003eType 4\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ecities are\u003cstrong\u003e\u0026nbsp;mobile locally to obtain better\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ediagnosis/treatment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4a conceptualizes the four types of cities regarding the function of inter-urban healthcare demand. Comprising only 4% of Chinese cities, \u003cem\u003eType 1\u003c/em\u003e cities create 27% of the inter-urban healthcare demand, among which most is for the best diagnosis/treatment by their residents moving nationwide. Not rich in quality healthcare and comprising 9% of Chinese cities, \u003cem\u003eType 2\u003c/em\u003e cities demand 20% of the inter-urban healthcare resources by their residents travelling regionwide for better diagnosis/treatment. The remaining 87% of cities are \u003cem\u003eType 3\u003c/em\u003e and \u003cem\u003e4\u003c/em\u003e cities, which demand 53% of the inter-urban healthcare resources (Fig. 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInter-dependent function and the benefit structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patient flows among different types of cities reflect the inter-dependency between healthcare providers and healthcare demanders, with flow size indicating intensity of interaction (Fig. 5). National hub cities serve \u003cem\u003eType 4\u003c/em\u003e cities the most (15%) rather than the most disadvantaged \u003cem\u003eType 3\u003c/em\u003e cities, indicating that accessibility to healthcare-rich cities offsets the local shortage of healthcare. Meanwhile, regional hub cities provide healthcare substantially to both \u003cem\u003eType 3\u003c/em\u003e and \u003cem\u003e4\u003c/em\u003e cities. The advantaged \u003cem\u003eType 1\u003c/em\u003e cities depend substantially on occasional destination cities to provide healthcare to their residents (9%). One possible reason is that \u003cem\u003eType 1\u003c/em\u003e cities, mostly China\u0026rsquo;s first-tier megacities, have a concentration of jobs involving frequent travelling\u003csup\u003e32\u003c/sup\u003e. Therefore, they generate healthcare demands during business trips to a wide range of cities\u003csup\u003e33-34\u003c/sup\u003e. The unnoticed pseudo-destination cities, notably, function to serve the advantaged \u003cem\u003eType 1\u003c/em\u003e and \u003cem\u003e2\u003c/em\u003e cities, which host the majority of China\u0026rsquo;s migrant workers.\u003c/p\u003e\n\u003cp\u003eWe discern the benefit structure of the IHS by examining the dual roles of cities. Combining the provision and demand roles yields nine compound modes involving more than three cities (Fig. 6a). For each city, we consider (1) the sufficiency of the local healthcare provision for the local population, and (2) the ability to obtain inter-urban healthcare provision for the local population. As a city both gains and loses healthcare resources within the IHS, we measure each city\u0026rsquo;s relative gains of inter-urban healthcare resources (see Methods). The results indicate significant differences in the two attributes among different compound modes (Fig. 6b-c). Accordingly, we infer which compound modes benefit from the IHS regarding ensuring healthcare provision to the local population.\u003c/p\u003e\n\u003cp\u003eReferring to Figure 6a, cities\u0026nbsp;at the junction of \u003cem\u003eType A\u003c/em\u003e and \u003cem\u003eType 1\u0026nbsp;\u003c/em\u003eare labelled\u0026nbsp;as \u003cstrong\u003e\u003cem\u003emagnates\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ebecause they leverage the nationwide IHS to supplement local healthcare provision, which is already the best. Accordingly, cities\u0026nbsp;at the junction of \u003cem\u003eType B\u003c/em\u003e and \u003cem\u003eType 2\u003c/em\u003e are\u0026nbsp;\u003cstrong\u003e\u003cem\u003elocal\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u003cem\u003emagnates\u003c/em\u003e\u003c/strong\u003e. They benefit less from the IHS and depend mainly\u0026nbsp;on\u0026nbsp;a regional base. We describe cities at the junction of \u003cem\u003eType D\u0026nbsp;\u003c/em\u003eand \u003cem\u003eType\u003c/em\u003e \u003cem\u003e2\u003c/em\u003e as \u003cstrong\u003e\u003cem\u003efree-riders\u003c/em\u003e\u003c/strong\u003e in that they do not contribute local resources to the IHS while obtaining inter-urban resources to compensate for the local shortage of quality healthcare.\u003c/p\u003e\n\u003cp\u003eWe refer to cities at the junction of \u003cem\u003eType C\u003c/em\u003e and \u003cem\u003eType\u003c/em\u003e \u003cem\u003e4\u003c/em\u003e as \u003cstrong\u003e\u003cem\u003etraders\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ebecause they offer local resources to remediate the megacities\u0026rsquo; supply deficiency for meeting the healthcare needs of migrant workers while obtaining inter-urban resources to serve local residents. They thus exchange benefits with other cities in the IHS. Similarly, cities at the\u0026nbsp;junction\u0026nbsp;of \u003cem\u003eType D\u003c/em\u003e and \u003cem\u003eType\u003c/em\u003e \u003cem\u003e4\u003c/em\u003e are also \u003cstrong\u003e\u003cem\u003etraders\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ethat exchange their service function of meeting occasional healthcare demand\u0026nbsp;from megacities for the supply to their local populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast,\u0026nbsp;\u003cstrong\u003e\u003cem\u003erefuges\u003c/em\u003e\u003c/strong\u003e, which refers to cities at the\u0026nbsp;junction of \u003cem\u003eType\u0026nbsp;\u003c/em\u003e\u003cem\u003eC\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Type 3,\u0026nbsp;\u003c/em\u003ecan scarcely benefit from the IHS; they do not obtain inter-urban resources to offset their shortage of supply to the local population, while providing substantially local resources to returning migrant workers. At the junction of \u003cem\u003eType D\u0026nbsp;\u003c/em\u003eand \u003cem\u003eType\u003c/em\u003e \u003cem\u003e3\u003c/em\u003e, \u003cstrong\u003e\u003cem\u003ecaretakers\u003c/em\u003e\u003c/strong\u003e are also disadvantaged in the IHS. They do not obtain additional supply from other cities\u0026nbsp;to serve local populations while providing the\u0026nbsp;scarce\u0026nbsp;local resources to meet the occasional healthcare demand\u0026nbsp;from\u0026nbsp;megacities. \u003cstrong\u003e\u003cem\u003eGivers\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eare cities at the junction of \u003cem\u003eType B\u0026nbsp;\u003c/em\u003eand \u003cem\u003eType\u003c/em\u003e \u003cem\u003e4\u003c/em\u003e, which share self-sufficient local resources with nearby cities but obtain few resources from the IHS.\u003c/p\u003e\n\u003cp\u003eIn total,\u0026nbsp;\u003cstrong\u003e\u003cem\u003emagnates, local magnates,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eand \u003cstrong\u003e\u003cem\u003efree-riders\u0026nbsp;\u003c/em\u003e\u003c/strong\u003econstitute the winners of the IHS,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003erespectively comprising\u0026nbsp;4%,\u0026nbsp;4%,\u0026nbsp;and\u0026nbsp;5%\u0026nbsp;of Chinese cities and accommodating\u0026nbsp;16%,\u0026nbsp;8%\u0026nbsp;and\u0026nbsp;8%\u0026nbsp;of the Chinese population.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eOn the other side are\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003erefuges\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003ecaretakers\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e,\u003c/em\u003e and \u003cstrong\u003e\u003cem\u003egivers\u003c/em\u003e\u003c/strong\u003e, which respectively comprise\u0026nbsp;24%,\u0026nbsp;7%,\u0026nbsp;and\u0026nbsp;4%\u0026nbsp;of Chinese cities and accommodate\u0026nbsp;13%,\u0026nbsp;6%,\u0026nbsp;and\u0026nbsp;6%\u0026nbsp;of the Chinese population. \u003cstrong\u003e\u003cem\u003eTraders\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(27%)\u003cem\u003e\u0026nbsp;\u003c/em\u003eare in an ambiguous position in the benefit structure. Interestingly, a majority (24%) of the cities are at the junction of \u003cem\u003eType O\u003c/em\u003e and \u003cem\u003eType\u003c/em\u003e \u003cem\u003e3;\u0026nbsp;\u003c/em\u003ethey accommodate\u0026nbsp;9% of the population of China and are \u003cstrong\u003e\u003cem\u003eoutsiders\u003c/em\u003e\u003c/strong\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eas they demand little from the IHS and provide nothing in return. The results point towards a Matthew-effect of accumulated advantage. The cities rich in local healthcare provision get richer through the IHS.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudies on patient mobility has informed policymaking on how to improve healthcare efficiency and equity.\u0026nbsp;However, the IHS behind the growing and diverse patient mobility remains largely unknown. This paper addresses\u0026nbsp;the\u0026nbsp;knowledge gap by utilizing human mobility\u0026nbsp;big data\u0026nbsp;to establish a typology of\u0026nbsp;roles of\u0026nbsp;Chinese cities based on their functions\u0026nbsp;in providing/demanding\u0026nbsp;inter-urban healthcare resources. Moreover, it unveils the benefit structure of the\u0026nbsp;IHS, emphasizing the imbalanced inter-urban dependence. The study draws three significant conclusions.\u003c/p\u003e\n\u003cp\u003eFirst, the interweaving of\u0026nbsp;three types of patient mobility in cities\u0026nbsp;shapes the IHS. The pseudo-patient mobility by migrant workers is highlighted\u0026nbsp;which adds complexity to the role of cities in providing and demanding healthcare. The fast yet unequal urbanization in China and other developing countries\u003csup\u003e8-12\u003c/sup\u003e, which involves massive labor flows into a few megacities and the\u0026nbsp;institutional\u0026nbsp;exclusion of migrant workers from the urban welfare system, poses significant challenges to ensuring equal rights to adequate healthcare. Pseudo-patient mobility not only reflects the unfair urbanization but also highlights the unjust exploitation of healthcare resources in less developed cities to sustain the economy and labor regime of developed megacities.\u003c/p\u003e\n\u003cp\u003eSecond,\u0026nbsp;cities\u0026rsquo; roles show distinct disparities regarding inter-urban healthcare provision and demand.\u0026nbsp;A small proportion (8%) of cities, specifically national and regional hubs, play a role in addressing the healthcare shortage in other cities, while a majority (63%) of cities compensate for the healthcare rights migrant workers are denied in megacities. However, China\u0026apos;s current national healthcare reform focuses primarily on strengthening the function of regional hub cities, thus overlooking the significance of pseudo-destination cities in promoting equity in healthcare access. The study suggests that the\u0026nbsp;unnoticed\u0026nbsp;pseudo-destination cities should receive attention and investments\u0026nbsp;to enhance their\u0026nbsp;function. This is crucial considering the uneven urbanization pattern in China and the institutionalized spatial mismatch between work and welfare provision for migrant workers.\u003c/p\u003e\n\u003cp\u003eThird,\u0026nbsp;cities are involved unequally in the IHS.\u0026nbsp;The IHS exhibits a\u0026nbsp;\u003cstrong\u003eMatthew effect,\u0026nbsp;\u003c/strong\u003ewhere a few already advantaged cities benefit more and can strengthen their structural advantages in meeting the healthcare demand of the local population.\u0026nbsp;In contrast, the majority of cities, particularly those facing healthcare shortages, are further disadvantaged in ensuring adequate healthcare for their local populations. While the IHS offers the potential for compensating and supplementing the local healthcare provision, the already advantaged cities take greater advantage of this opportunity, especially in relation to providing healthcare for migrant workers. Accordingly, the IHS generates new structural inequalities in healthcare access while addressing the spatial mismatch between healthcare provision and demand. Policies aimed at enhancing healthcare access equity should consider improving the benefit structure of the system toward more balanced configurations. This includes transforming the position of disadvantaged cities, including \u003cem\u003erefuges\u003c/em\u003e, \u003cem\u003ecaretakers\u003c/em\u003e, and \u003cem\u003egivers\u003c/em\u003e, to more advantaged ones, like \u003cem\u003etraders\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile providing a new perspective of the urban system to tackle equity in healthcare access, this study enhances our understanding of the vertical (function beyond job creation and economic agglomeration) and the horizontal (inter-dependent function between cities) extension of cities\u0026apos; roles in urban systems. Manuel Castells\u003csup\u003e35\u003c/sup\u003e introduced the concept of \u003cem\u003espace of flows\u003c/em\u003e to redefine a place as a nexus of flows of people, capital, goods, and information. Cities develop inter-dependent functions upon these flows\u003csup\u003e36\u003c/sup\u003e. Previous studies have focused on how inter-urban economic flows interweave to form an urban system and to shape the \u003cem\u003eglobal cities\u003c/em\u003e as centers of economic command and control\u003csup\u003e19, 21\u003c/sup\u003e. However, our study reveals that cities also extend their functions to provide or demand healthcare mutually alongside the increasing mobility of populations seeking healthcare. The study highlights the interconnectedness of healthcare provision and demand within the urban system, emphasizing the importance of understanding cities\u0026apos; extended roles beyond economic functions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData on human mobility\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCellphones communicate with transceivers regularly, and the communication log can be used to decipher the locations and\u0026nbsp;moving\u0026nbsp;trajectories of users at a full temporal coverage\u003csup\u003e37\u003c/sup\u003e.\u0026nbsp;In this study,\u0026nbsp;we used a set of cellphone-user\u0026nbsp;trajectory data collected between 1\u003csup\u003est\u003c/sup\u003e and 31\u003csup\u003est\u003c/sup\u003e December\u0026nbsp;2022 from\u0026nbsp;China Unicom,\u0026nbsp;which is one of China’s\u0026nbsp;top three telecom companies\u0026nbsp;whose cellphone users comprise about 20% of the market share in\u0026nbsp;China\u003csup\u003e38\u003c/sup\u003e.\u0026nbsp;The data cover over 300 million cellphone users in\u0026nbsp;China\u0026nbsp;during the study period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData on location and areas of hospitals\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn China, patients travel mainly for quality healthcare rather than basic healthcare\u003csup\u003e12, 39-40\u003c/sup\u003e. We thus include the non-local patients’ visits to the top hospitals in the analysis. In China, Tertiary-A hospitals are officially recognized by the Chinese government\u0026nbsp;as the hospitals offering the highest quality healthcare\u003csup\u003e12\u003c/sup\u003e. Hence, in this study, patient mobility is calculated as\u0026nbsp;a patient’s movement to utilize the healthcare of a Tertiary-A hospital located in a city other than the patient’s city of residence. A list of Tertiary-A hospitals was provided by the \u003cem\u003eNational Health Commission of the People’s Republic of China\u003c/em\u003e in 2022, which is the highest authority in charge of China’s national healthcare system. In total, 1,404 Tertiary-A hospitals were included. We searched the location of each Tertiary-A hospital on Baidu map\u0026nbsp;(https://map.baidu.com/)\u0026nbsp;and collected the area-of-interest (AOI) data of each\u0026nbsp;Tertiary-A hospital using a web crawler.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData on locational attributes of cities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analyses of this study involve the data on cities’ attributes related to cross-city healthcare utilization. For local healthcare provision level, the number of hospital beds is widely used as a measure\u003csup\u003e9, 12\u003c/sup\u003e, and we collected the data from official website of each hospital. We measured local economic development level by the annual GDP of 2022 and collected the data from China Statistical Yearbook (https://www.stats.gov.cn). We measured local transportation accessibility by the degree centrality of cities in national railway networks and collected the data from the online ticketing system of China's railways administration (https://www.12306.cn/index/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient mobility identification\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used a three-step analysis (see\u0026nbsp;Supplementary Fig.\u0026nbsp;13) to identify patient mobility from the\u0026nbsp;trajectory data\u0026nbsp;and AOI data: (1) a residence city is calculated as the city in which a cellphone user had the longest stop during nighttime (from 9 p.m. to 8 a.m.)\u0026nbsp;over the previous 12 months; (2) a trans-city healthcare utilization is calculated as a cellphone user’s visit to the area of a Tertiary-A hospital not in the residence city, and the stay in the area of the Tertiary-A hospital was over 30 minutes; and (3) a non-local patient is defined as a cellphone user who visited and stayed in the area of a Tertiary-A hospital not in the residence city for over 30 minutes.\u003c/p\u003e\n\u003cp\u003eFinally, we identified almost 19 million (18,993,351) visits to\u0026nbsp;1,404\u0026nbsp;Tertiary-A hospitals in China from over 300\u0026nbsp;million cell phone users’\u0026nbsp;daily movements generated in December 2022, among which 1,984,448 were trans-city healthcare utilizations made by non-local patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassifying patient mobility based on the purpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the literature, there are three main types of patient mobility in China, and we used a three-step method to identify them (Supplementary Fig. 14). The first follows the classical pull-push theory of amenity-driven migration\u003csup\u003e41\u003c/sup\u003e whereby a patient travels to another city for better treatment/diagnosis. Accordingly, we calculate the patient mobility from the residence city to a city richer in quality healthcare as the \u003cstrong\u003emobility for better treatment/diagnosis\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe second is when a patient travels to another city for the best treatment/diagnosis, which is common especially among the wealthy populations\u003csup\u003e6\u003c/sup\u003e. We thus calculate the patient mobility from a residence city that is\u0026nbsp;the\u0026nbsp;richest\u0026nbsp;in quality healthcare to another city that is also\u0026nbsp;the\u0026nbsp;richest in quality healthcare\u0026nbsp;as the \u003cstrong\u003emobility for the best treatment/diagnosis\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe third is when a migrant worker returns from the city where they work and reside to their home city for healthcare, and the home city is less rich in healthcare resources than the host city, which is\u0026nbsp;inconsistent with the push-pull theory\u003csup\u003e8\u003c/sup\u003e. This emerged especially in developing countries that have fast and uneven urbanization. China’s urbanization since the 1990s was characterized by the inflow of massive populations from villages, towns, and small cities to megacities for job opportunities\u003csup\u003e42\u003c/sup\u003e, while the migrant workers have largely been excluded from the megacities’ social welfare system including the healthcare insurance system\u003csup\u003e26\u003c/sup\u003e. Thus, migrant workers return to the home city for healthcare during holidays and work leave\u003csup\u003e27\u003c/sup\u003e. The\u0026nbsp;4\u0026nbsp;first-tier cities (i.e., Beijing, Shanghai, Shenzhen, and Guangzhou), another 2\u0026nbsp;municipalities directly under the central government (i.e., Tianjin and Chongqing),\u0026nbsp;and 22 provincial capital cities (excluding Haikou, Yinchuan, Lhasa and Hohhot), where over 110 million of the migrant workers are concentrated, host over 30% of all the floating populations in China\u003csup\u003e43\u003c/sup\u003e. They are also rich in quality healthcare (Supplementary Fig. 15) and have the strict \u003cem\u003ehukou\u003c/em\u003e-based welfare system that excludes migrant workers. We thus calculate the mobility of patients who reside in the 28 major cities to the cities\u0026nbsp;which are prominently deprived of quality healthcare\u0026nbsp;as the \u003cstrong\u003epseudo-patient mobility\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA city’s lack/richness of quality healthcare is measured by the capacity of all the Tertiary-A hospitals of the city, and the capacity of a Tertiary-A hospital is measured by the number of hospital beds. We use the Jenks natural breaks algorithm to determine the threshold value of the level of capacity of quality healthcare. The cities in the category of the highest level (level 1) of Tertiary-A hospital capacity are defined as being the richest in quality healthcare,\u0026nbsp;and the cities in the last two levels\u0026nbsp;(3 and 4)\u0026nbsp;are defined as being significantly deprived regarding quality healthcare and are recognized as the destination of pseudo-patient mobility from the 28 major cities (Supplementary Fig. 15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClustering cities based on the dual roles in trans-city healthcare utilization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe cluster all the 364 Chinese cities according to their role as the origin (demand) and destination (provision) of patient mobility, respectively. All 364 cities had an outflow of patients, and they are all included in the clustering analysis of origin cities. However, only 273 cities had an inflow of non-local patients, and they are thus included in the clustering analysis of destination cities. The remaining 91 cities hosting no non-local patients are placed into one group manually.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor clustering destination cities, we consider three dimensions of the seven attributes that depict a city’s capacity for serving non-local patients (Supplementary Table 1):\u0026nbsp;(1) scope of services, including (1a) vertical scope regarding the geographical depth of catchment areas, which is measured by the weighted average distance between the destination city and the origin cities of the served non-local patients, and (1b) horizontal scope regarding the diversity of origin cities, which is measured by the number of origin cities of the served non-local patients; (2) necessity of services, including (2a) healthcare deprivation of served origin cities,\u0026nbsp;which is measured by the weighted number of Tertiary-A hospital beds per 1,000 persons of origin cities of the served non-local patients; and (2b) economic level of served origin cities, which is measured by the weighted GDP per capita of origin cities of the served non-local patients; (3) type of mobility of the inflowing patients, which is measured by percentage of mobility for the best, for better diagnosis/treatment, and pseudo-patient mobility, respectively\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor clustering origin cities, we consider four dimensions of the seven attributes that depict the ability and motivation of a city’s residents to move to access non-local quality healthcare (Supplementary Table 2): (1) scope of mobility including (1a) vertical scope regarding the geographical depth of destination cities which is measured by weighted average distance between the origin city and the destination cities of outflowing non-local patients, and (1b) horizontal scope regarding the diversity of destination cities, which is measured by the number of destinations of outflowing non-local patients; (2) motivation to move regarding the deprivation of the local quality healthcare, which is measured by the number of Tertiary-A hospital beds per 1,000 persons; (3) economic ability to move, which is measured by GDP per capita of the city; and (4) type of mobility of the outflowing patients, which is measured by percentage of mobility for the best, for better diagnosis/treatment, and pseudo-patient mobility, respectively\u003c/p\u003e\n\u003cp\u003eThe bisecting K-Means is used for clustering cities.\u0026nbsp;This is an unsupervised clustering algorithm that is based on the traditional K-Means method but is deemed a more efficient and more accurate technique\u003csup\u003e44\u003c/sup\u003e. As a divisive hierarchical clustering algorithm, the bisecting K-Means assigns all data points to one cluster at first, and the K-Means is repeatedly applied to the parent cluster, which is known as ‘C’ in order to obtain the best subclusters, which are then known as ‘C1’ and ‘C2’\u003csup\u003e45\u003c/sup\u003e. The formal procedure of the bisecting K-Means is as follows: (1) assigning a parent cluster C to be split; (2) using K-Means to split C into subclusters C1 and C2; (3) calculating the inter-cluster similarity (e.g., Euclidean distance) for C1 and C2 until a fixed number of iterations is achieved; (4) selecting the subclusters with the highest inter-cluster dissimilarity (e.g., biggest sum of squared errors); and (5) repeating steps (2) and (3) until k clusters are formed.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;elbow index (sum of squared errors) and the Silhouette Coefficient\u0026nbsp;are used in the bisecting K-Means to determine the number of clusters, and the best cluster number is 4 both for the origin and for the destination cities (Supplementary Fig. 16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasuring relative gains in inter-urban healthcare resources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere, we assume that the IHS operates similarly to the trade system where a city both imports and exports healthcare resources. The patient outflows and inflows indicate the volume of import and export. Hence, we introduce the concept of Vollrath’s revealed comparative advantage\u003csup\u003e46\u003c/sup\u003e to construct the \u003cem\u003erelative import advantage\u0026nbsp;\u003c/em\u003eindex (Formula 1) to measure a city’s relative advantage in obtaining inter-urban healthcare resources, and the \u003cem\u003erelative export advantage\u0026nbsp;\u003c/em\u003eindex (Formula 2) to measure a city’s relative advantage in providing inter-urban healthcare resources. Finally, we use the ratio of \u003cem\u003erelative import advantage\u003c/em\u003e to \u003cem\u003erelative export advantage\u003c/em\u003e (Formula 3) to measure a city’s relative gains in IHS. The larger the value, the more a city gains from IHS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the financial support for this research provided by National Natural Science Foundation of China (41925003, 42301220)，\u0026nbsp;Shenzhen science and technology program (JCYJ20220818100810024, KQTD20221101093604016,\u0026nbsp;RCBS20221008093306009);\u0026nbsp;Guangdong Basic and Applied Basic Research Foundation (2023A1515010875); Humanities and Social Sciences Fund of the Ministry of Education of China (Grant Number: 23YJC840037).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.L. contributed to conceptualization, methodology, data curation, formal analysis, visualization, result interpretation, writing-original draft, writing-review and editing, and funding acquisition. P.Z. contributed to writing-review and editing, supervision, and funding acquisition. M.Z. contributed to conceptualization, result interpretation, writing-original draft, writing-review and editing, and funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStevenson, M., Thompson, J., de S\u0026aacute;, T. H., Ewing, R., Mohan, D., McClure, R., ... \u0026amp; Woodcock, J. (2016). Land use, transport, and population health: estimating the health benefits of compact cities. \u003cem\u003eThe lancet\u003c/em\u003e, \u003cem\u003e388\u003c/em\u003e(10062), 2925-2935.\u003c/li\u003e\n\u003cli\u003eConnolly, C., Keil, R., \u0026amp; Ali, S. H. (2021). 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A theoretical evaluation of alternative trade intensity measures of revealed comparative advantage. \u003cem\u003eWeltwirtschaftliches Archiv\u003c/em\u003e\u003cstrong\u003e127\u003c/strong\u003e, 265\u0026ndash;280. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"healthcare access, patient mobility, urban system, role of cities, human mobility","lastPublishedDoi":"10.21203/rs.3.rs-4837017/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4837017/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCities are increasingly interdependent regarding healthcare provision/demand. However, the inter-urban healthcare system (IHS) behind the nationwide patient mobility remains largely unknown. Leveraging human mobility big data, we reveal cities’ roles in providing/demanding quality healthcare within the IHS of China. We find that 8%of Chinese cities arenational and regional hubs that address the healthcare shortage of cities deprived of quality healthcare, while 63% of the cities that are unnoticed compensate for migrant workers being denied healthcare rights in megacities. IHS generates new structural inequalities in healthcare access exhibiting a Matthew effect,\u003cstrong\u003e \u003c/strong\u003ewhere the few (12%) cities that are already rich in healthcare resources benefit more and can strengthen their advantages in providing healthcare to local populations (32% of China’s total population). While, the majority (35%) of cities, particularly those facing healthcare shortages, are further disadvantaged in ensuring adequate healthcare for their local populations (26% of China’s total population).\u003c/p\u003e","manuscriptTitle":"Unequal roles of cities in the inter-urban healthcare system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-23 12:34:57","doi":"10.21203/rs.3.rs-4837017/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-cities","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcities","sideBox":"Learn more about [Nature Cities](https://www.springer.com/journal/44284)","snPcode":"44284","submissionUrl":"","title":"Nature Cities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"54904de6-91c0-49b0-b0d5-a7e597424382","owner":[],"postedDate":"August 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36321763,"name":"Social science/Geography"},{"id":36321764,"name":"Scientific community and society/Geography"}],"tags":[],"updatedAt":"2025-01-03T08:09:00+00:00","versionOfRecord":{"articleIdentity":"rs-4837017","link":"https://doi.org/10.1038/s44284-024-00185-8","journal":{"identity":"nature-cities","isVorOnly":false,"title":"Nature Cities"},"publishedOn":"2025-01-02 05:00:00","publishedOnDateReadable":"January 2nd, 2025"},"versionCreatedAt":"2024-08-23 12:34:57","video":"","vorDoi":"10.1038/s44284-024-00185-8","vorDoiUrl":"https://doi.org/10.1038/s44284-024-00185-8","workflowStages":[]},"version":"v1","identity":"rs-4837017","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4837017","identity":"rs-4837017","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}