Analyzing Catchment Areas for Home Medical Care Services Using Real-world Claims Data

Analyzing Catchment Areas for Home Medical Care Services Using Real-world Claims Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analyzing Catchment Areas for Home Medical Care Services Using Real-world Claims Data Yasuhiro Morii, Yasuhiro Nakanishi, Yuichi Nishioka, Yukio Tsugihashi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7140322/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background When evaluating the supply-demand balance of home medical care services (HMCS), it is important to consider catchment areas, as the distance to patients may influence healthcare providers’ behavior. This study aims to examine analytical methods using a medical claims database to identify catchment areas for HMCS, to better assess their supply-demand balance. Methods The subjects were 39 municipalities in Nara prefecture, Japan, as a model case. The information on patients and hospitals or clinics was obtained from medical claims in the prefecture-wide (KDB) database in FY 2019. The travel distances from hospitals or clinics to patients were analyzed using a geographical information system (GIS) for three categories of HMCS: (1) cases where the Home Medical Care Management Fee (HMCMF) was charged, (2) cases where the Facility Admission Medical Care Management Fee (FAMCMF) was charged, and (3) all other cases. The catchment areas were aggregated for three groups based on population density, from both the facility and patient perspectives. In addition, utilization rates and the number of hospitals or clinics providing HMCS were also aggregated. Results The number of facilities for HMCMF and FAMCMF was limited, and the utilization rates for these services were lower in sparsely populated municipalities such as Nanwa in the southern part. From the facility perspective, the catchment areas were 3.4, 3.1, and 2.1 km for HMCMF, 8.9, 7.6, and 4.3 km for FAMCMF, and 3.8, 2.5, and 2.8 km for the other category in the low, middle, and high population density groups, respectively. Conclusions The catchment areas for HMCS were analyzed using the KDB database and GIS. In Nara Prefecture, the catchment areas were broader for FAMCMF than for the other categories. The catchment areas did not differ much between the population density groups from the facility perspective. The results also indicate that, particularly for HMCMF and FAMCMF, the needs in some sparsely populated municipalities may not be adequately met. Home medical care Geographical accessibility Geographical information system Medical claims database Catchment area Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Home medical care services (HMCS) are of greater importance in countries with aging population such as Japan, which has the highest aging rate in the world [ 1 ]. Medical demands for HMCS will increase with the aging of society in Japan until around 2040 [ 2 ]. In Japan, it is required that prefectural governments make Regional Medical Plan to ensure that medical services including HMCS are reached to meet needs from local population by optimally allocating healthcare resources [ 3 – 4 ]. Also, the prefectures are required to consider regional units that are appropriate in providing HMCS to residents in each area while taking into account resource constraints. Geographical accessibility is one of the important factors in considering optimal allocation of healthcare resources on HMCS since healthcare providers need to visit patients’ houses or facilities where patients reside. Few studies have been conducted on the geographical accessibility to HMCS while there are several studies for other medical services [ 5 – 7 ]. We have previously reported the results of an analysis of the geographical accessibility to HMCS in Nara prefecture of Japan, utilizing the National Health Insurance Database (Kokuho database: KDB), a prefecture-wide database that includes both medical and long-term care claims data [ 8 ]. Using a Geographic Information System (GIS), we demonstrated the applicability of the KDB database for analyzing geographical accessibility by conducting an analysis based on hypothetical patients under the following two scenarios: (1) in which healthcare providers travel to hypothetical patients (randomly distributed according to the distribution of general population aged over 75) from the closest of all clinics or hospitals in Nara prefecture as an ideal case and (2) in which healthcare providers travel to hypothetical patients from the closest of the hospitals or clinics which actually providing HMCS identified from KDB analysis. We clarified a regional disparity in geographical accessibility in a sparsely populated area in the southern part of the prefecture and clarified that in the ideal case where all hospitals or clinics provided the services, the disparity would be improved. In addition to our previous research findings, it is also important to clarify the actual catchment areas where healthcare providers deliver HMCS by using the actual patient locations rather than hypothetical ones, to consider the optimal allocation of supply. It is necessary to demonstrate that such analysis is feasible using KDB database. Conventionally, the balance of supply and demand or needs is evaluated by simple indicators such as supply/demand ratio (e.g., the number of physicians per population). However, these indicators do not take geographical aspects into account. In the Japanese universal healthcare system, HMCS need to be provided within 16 km from hospitals or clinics unless there is a justifying reason [ 9 – 10 ]. However, hospitals or clinics do not necessarily travel up to 16 km due to limited human resources or operational efficiency. Therefore, when considering optimal allocation of healthcare resources, we need to understand how long healthcare providers practically travel. It is possible that the catchment areas are different among regions and types of services. If we understand the catchment areas, that enables us to perform more analysis of supply and demand with deeper indication such as two-step floating catchment area (2SFCA) methods, which weigh supply using distance decay functions. The purpose of this study is to examine analytical methods using a medical claims database (KDB database) to evaluate the supply-demand balance of home medical care services (HMCS) by clarifying the catchment areas. This study, using the real-world database of claims (KDB), will help consider optimal allocations of healthcare resources related to HMCS. Methods Subjects and scheme In this study, we analyzed the KDB, a medical claims database from 39 municipalities in Nara prefecture, Japan, which served as a model area. Information on the geographic locations of the prefecture inside Japan, the secondary areas, and the municipalities was visualized in a previous study [ 8 ]. Nara prefecture has a population of 1,286,651, a land area of 3,691 km 2 (1% of Japan’s land), and an aging rate of 32% [ 11 – 12 ] (See Table 1 and Fig. 1 of the previous study [ 8 ]. The southern part (Nanwa secondary medical area) is the most depopulated rural region, and there are also a few depopulated rural municipalities in the eastern part. In this study, the 39 municipalities in Nara were categorized into three groups based on their population densities [ 12 , 13 ], as catchment areas can differ between populated and depopulated areas. The municipalities included in each group are shown in Additional File 1, and the geographical distribution is illustrated in Fig. 1 -a. For each group, the travel distance between hospitals or clinics providing HMCS to patients was analyzed using GIS (Esri, ArcGIS Desktop version 10.8.1) based on the utilization data obtained from KDB analysis. Figire 1 shows the distribution of municipalities included in each group based on population density (a) and the distributions of hospitals or clinics providing HMCS services identified by KDB analysis for (b)HMCMF, (c) FAMCMF, and (d) others KDB (claims database) analysis The information on patients and hospitals or clinics was obtained from an analysis of the healthcare claims in the KDB database of Nara prefecture in FY 2019. The locations of patients aged 75 years or older who lived in Nara prefecture and utilized HMCS in the year were obtained in the form of zip codes. In addition, the institutional codes of hospitals or clinics in Nara from which the patients received HMCS were obtained; the addresses were obtained from a published source [ 14 ]. The utilization or provision of HMCS was defined as having a claim for HMCS [ 8 ]. The aforementioned analysis was performed for three service categories: (1) patients for whom the Home Medical Care Management Fee (HMCMF) was charged; (2) patients for whom the Facility Admission Medical Care Management Fee (FAMCMF) was charged; and (3) and other cases. HMCMF and FAMCMF are applicable when a comprehensive home care plan is created for each patient, regular visits are made for medical examinations, and comprehensive medical management is provided at home and in a long-term care facility respectively by Home Medical Care Support Clinic or Home Medical Care Support Hospital. They are authorized by the Ministry of Health Labour and Welfare (MHLW) with requirements such as 24-hour operation for HMCS and comprehensive management of patients. Home Medical Care Support Clinic or Home Medical Care Support Hospital are expected to play a central role in regional HMCS provision. The utilization rate is defined by the number of claims divided by the population aged 75 years or older. The number of hospitals was aggregated by municipalities for each category. These aggregations were performed using R ver 4.1.2. Geographical Analysis In this analysis, travel distance (i.e., catchment area) from hospitals or clinics that provided HMCS to patients receiving the services (i.e., the travel distance between the actual pair of a hospital or clinic and a patient identified by the KDB analysis) was analyzed. First, the locations of patients based on their zip codes were mapped on GIS as geographically representative points using geocoding function. Next, the locations of hospitals or clinics were mapped based on their addresses using the geocoding function. The travel distance was analyzed using the “OD cost matrix” function; that is, travel distance between every single pair of hospital or clinic and patient was calculated. It is assumed that one claim consisted of one trip. ArcGIS Geo Suite Road Network (ESRI Japan) was used for the analysis. Median and percentiles of travel distances were aggregated for the groups by population densities, and the median was also aggregated by municipalities, both from the facilities’ and patients’ perspectives, for each category. Catchment areas from the facility perspective indicated how far healthcare providers in the area traveled (from hospitals or clinics) to deliver care; catchment areas from the patient perspective indicatedhow far patients were located from the healthcare providers who delivered HMCS. Catchment areas were calculated for three population density groups (high, middle, and low). In addition, the rates of patients within the same municipalities from the two perspectives were calculated. Results The distribution of hospitals or clinics providing HMCS by the service categories is shown in Figs. 1 -b, -c, and -d. Three small municipalities had no facilities providing any type of HMCS. Additionally, municipalities in the depopulated Nanwa (southern) area and some other small municipalities (villages) in other regions had fewer facilities providing HMCS, particularly HMCMF and FAMCMF. Overall, in Nara prefecture, 346, 121, and 261 hospitals or clinics and 24,551, 36,764, and 19,665 claims were identified as providing HMCS for HMCMF, FAMCMF, and others, respectively. The number of hospitals or clinics providing HMCS services, the number of claims in each category, and utilization rates are summarized in Table 1 for each population density group. The high-population-density group had higher numbers of hospitals or clinics, claims, while the low-population-density group had lower numbers, particularly for FAMCMF. The utilization rates in each municipality are illustrated in Fig. 2 . The utilization rates in Nara overall were 0.101, 0.152, and 0.081 for HMCMF, FAMCMF, and others, respectively. Figure 2 shows the utilization rates of (a)HMCMF, (b) FAMCMF, and (c) others by municipalities. The rates were presented as percentiles among the 39 municipalities. Table 1 Aggregated characteristics for the population-density groups low density group middle density group high density group # of hospitals or clinics HMCMF 11 32 125 FAMCMF 2 21 97 others 21 61 179 # of claims from facility perspective HMCMF 471 6847 17233 FAMCMF 411 5320 30782 others 648 4735 14262 # of claims from patient perspective HMCMF 1132 5796 17623 FAMCMF 966 5440 30319 others 833 4645 14167 Utilitzation rate (claims/population aged 75 years or older) HMCMF 0.082 0.110 0.101 FAMCMF 0.070 0.104 0.174 others 0.060 0.088 0.081 Table legend: Table 1 shows the number of hospitals or clinics, claims, and the utilization rates by types of HMCS services, aggregated from the KDB database analysis Figure 3 shows the rate of claims for patients who lived within the same municipalities from the facilities’ perspective. The rates were lower for FAMCMF than for other categories. Figure 4 shows the rate of claims for patients who lived within the same municipalities from the patients’ perspective. The rates were lower in the municipalities in the depopulated Nanwa area and some other small municipalities (villages) in other regions. In Nara prefecture overall, the catchment areas were 2.2 (IQR: 1.1-4.4), 4.6 (IQR: 2.1-8.1), and 3.1 (IQR: 1.3-5.9) km for HMCMF, FAMCMF, and others, respectively. The travel distances in the three groups from the facility perspective are visualized in Figure 5. For HMCMF, the travel distances were 3.4 (IQR: 1.5-5.1), 3.0(IQR: 1.3-6.7), and 2.1(IQR: 1.1-3.8) km in the low-, middle-, and high-population-density groups, respectively (Figure 5-a). For FAMCMF, the travel distances were 8.9 (IQR: 5.3-11.1), 7.6 (IQR: 2.7-14.5), and 4.3 (IQR: 2.0-7.2) km in the low-, middle-, and high-population-density groups, respectively (Figure 5-b). For the others category, the travel distances were 3.8 (IQR: 1.8-8.4), 2.5 (IQR: 1.1-5.6), and 2.8 (IQR: 1.3-6.0) km in the low-, middle-, and high-population-density groups, respectively (Figure 5-c). Figure 6 shows the travel distances from the facilities’ perspective by municipalities. The travel distances were basically not much different among the groups or municipalities. The travel distances in the three groups from the patients’ perspective are visualized in Figure 7. For HMCMF, the travel distances were 9.0 (IQR: 2.6-13.7), 2.3 (IQR: 1.1-4.7), and 2.1 (IQR: 1.1-4.0) km in the low-, middle-, and high-population-density groups, respectively (Figure 7-a). For FAMCMF, the travel distances were 13.5 (IQR: 8.9-23.2), 6.6 (IQR: 2.7-14.1), and 4.1 (IQR: 1.9-7.1) km in the low-, middle-, and high-population-density groups, respectively (Figure 7-b). For the others category, the travel distances were 5.7 (IQR: 1.8-8.4), 2.6 (IQR: 1.1-6.5), and 2.6 (IQR: 1.3-5.7) km in the low-, middle-, and high-population-density groups, respectively (Figure 7-c). Discussion This study analyzed the catchment areas of HMCS in Nara prefecture, Japan, as a model case, using actual patient location data obtained from the KDB database and GIS. By using KDB, a real-world database, we were able to perform catchment area analysis reflecting actual practices, which is crucial for determining the optimal allocation of HMCS-related healthcare resources. First, regarding HMCS supply, the results found that there was an inadequate number of hospitals and clinics providing HMCS in municipalities in the depopulated Nanwa area and some other small municipalities (villages) in other regions, particularly for HMCMF and FAMCMF. These fees apply to facilities authorized as Home Medical Care Support Clinics or Home Medical Care Support Hospitals that provide HMCS services regularly with comprehensive medical management. The results indicate that such facilities for HMCS, which can provide comprehensive medical management regularly, are scarce in these rural areas. The utilization rates were particularly lower in these rural areas for these categories. The disparity in the distribution of facilities could have led to the difference in the utilization rates. It has been previously reported that utilization rates of HMCS services overall were lower in these areas [8]. Adding to the research, our results clarified that the disparity was larger for HMCS with HMCMF and FAMCMF, which are considered comprehensive medical care services. MHLW reported that increasing the supply (i.e., the number of facilities providing HMCS) is a key issue in promoting the utilization of HMCS. In Nara prefecture, the issue could apply to rural areas, particularly for HMCMF and FAMCMF. A supply-demand balance for FAMCMF will also be of great importance in achieving a more integrated medical and long-term care. The travel distances for FAMCMF were longer than for the other two categories. A possible explanation is that more than one patient can be treated at a long-term care facility for FAMCMF; this is efficient for HMCS providers, even though travel time is relatively long. Further, HMCMF and the others category are mostly provided on an individual need basis. For FAMCMF, some patients were provided with the service from clinics or hospitals more than 16 km away. The catchment areas for HMCMF and the others category were not much different between the high-, middle-, and low-population-density groups; however, the interquartile ranges were narrower in the high-population-density group. It is understandable that, considering operative efficiency, catchment areas do not differ much on average, regardless of the area. The catchment areas for FAMCMF were smaller in the high population density group. Overall, in more densely populated areas, healthcare providers could cover demands within shorter distances. From the patients’ perspective, the travel distance was longer, or there were no patients in the rural municipalities in the depopulated Nanwa area and some other small municipalities (villages) in other regions. The utilization rates were lower in these municipalities. These results indicate that, in these municipalities, the needs for HMCS are unlikely to be met. In addition, the results showed that even when HMCS services were provided, those services were provided by facilities in other municipalities. The prefectures in Japan are required to consider appropriate regional units to ensure that HMCS services meet the needs of residents. The working group of HMCS in the Ministry of Health, Labour and Health stated that the regional units are basically municipalities, while units will be larger depending on available healthcare resources [3]. Our results can inform the establishment of optimal and practical regional units, given that current resource constraints make it difficult to ensure adequate supply in every municipality. In this study, the catchment areas were different between the categories, but not very much different among the population density groups. In general, the further a patient is from a facility, the less likely the facility is to provide services to the patient. Therefore, it is essential to take into account distance when comparing healthcare needs and supply (i.e., a simple indicator such as the supply-demand ratio is not always applicable, particularly to services requiring travel, such as HMCS). For example, the 2SFCA method weighs the supply using distance decay functions [9-10], which helps measure supply-demand ratios that reflect “real-life” healthcare behaviors. The results of catchment areas obtained in this research can be applied to evaluate the sufficiency of HMCS resources while considering travel. For example, the results could inform the estimation of a distance decay function when the 2SFCA method analysis is performed. The study methodology is universal and can be applied to other areas or countries, as well as to other fields of healthcare, when considering geographical accessibility and catchment areas. However, this study has several limitations. First, due to limited data, travel from one patient’s home to another was not considered. Second, this study only used KDB data from FY2019, which could cause some uncertainty in the results, particularly in smaller municipalities. Third, the patient address in claims does not guarantee that it is the place where the patient actually lives (e.g., it is possible that a patient enters a long-term care facility in municipality B, while his/her registered domicile remains at municipality A); although the rate is expected to be low. Future research should create algorithms to identify the discrepancy between the actual addresses and the registered domicile. Further, our analysis targeted Nara prefecture, and therefore, hospitals and clinics outside the prefecture were not considered. Lastly, while this study divided the subject municipalities into three groups based on population density, it is still not clear whether the results could be applied to metropolises since Nara prefecture does not have such cities. Likewise, the results for areas with low population density may not fully reflect the conditions in highly depopulated areas, as depopulated municipalities in Nara prefectures had limited numbers of hospitals or clinics providing and patients receiving HMCS services. Further research is required to clarify the catchment areas in metropolitan areas and highly depopulated areas. Conclusion This study analyzed real-world catchment areas—defined as travel distances from hospitals or clinics to patients—for three HMCS service categories, using the KDB database of Nara prefecture. The results clarified that the catchment areas were larger for FAMCMF services than for other categories. From the facility perspective, the catchment areas were not much different between the population density groups. The facilities for HMCMF and FAMCMF were scarce, and travel distances from the patient perspective were longer in municipalities in Nanwa and some other rural municipalities. Further, the utilization rates for these services were lower in these areas, and their services were provided by other municipalities. Therefore, the study concludes that, the need for these services, particularly HMCMF and FAMCMF, was likely unmet. Abbreviations HMCS Home medical care services GIS Geographical information system HMCMF Home Medical Care Management Fee FAMCMF Facility Admission Medical Care Management Fee 2SFCA Two-step floating catchment area MHLW Ministry of Health Labour and Welfare Declarations Clinical trial number : not applicable Ethics approval and consent to participate: This study was approved by the ethics committee in National Institute of Public Health, Japan（NIPH-IBRA#12324-2）. The Requirement for informed consent was waived because all the data were anonymized. Consent for publication: Not applicable Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests Funding: This study was partially supported by a Grant-in-Aid for Scientific Research from the Ministry of Health, Labour and Welfare, Japan. Authors' contributions: YM, YN, and MA designed the research plan; YT, TN, TM, and TI contributed significantly to the development of the research. YM performed the geographical analysis, and YN performed the claims database analysis. AM led the research project. YM drafted the manuscript, and all authors reviewed it. All authors read and approved the final manuscript. Acknowledgements: Not applicable References Cabinet Office. The current situation of aging of society. (2020). https://www8.cao.go.jp/kourei/whitepaper/w-2023/html/zenbun/s1_1_2.html (Accessed on September 7, 2024) (In Japanese). The Ministry of Health, Labour and Welfare. Constructing the structure of providing HMCS (May 24, 2023). https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000194369.html (Accessed on September 7, 2024) (In Japanese). The Ministry of Health, Labour and Welfare. HMCS -toward 8th regional medical plan. (October 13, 2021). https://www.mhlw.go.jp/stf/newpage_24354.html (Accessed on September 7, 2024) (In Japanese). The Ministry of Health, Labour and Welfare. Regional Medical Plan. https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/iryou/iryou_keikaku/index.html (Accessed on September 7, 2024) (In Japanese). Schwarz J, Hemmerling J, Kabisch N, Galbusera L, Heinze M, von Peter S, et al. Equal access to outreach mental health care? Exploring how the place of residence influences the use of intensive home treatment in a rural catchment area in Germany. BMC Psychiatry. 2022;22(1):826. Wang Y, Zhang Q, Spatz ES, Gao Y, Eckenrode S, Johnson F, et al. Persistent geographic variations in availability and quality of nursing home care in the United States: 1996 to 2016. BMC Geriatr. 2019;19(1):103. Naruse T, Matsumoto H, Fujisaki-Sakai M, Nagata S. Measurement of special access to home visit nursing services among Japanese disabled older adults: using GIS and claim data. BMC Health Serv Res. 2017;17(1):377. Morii Y, Nakanishi Y, Nishioka Y, Tsugihashi Y, Noda T, Myojin T et al. Analyzing Disparity in Geographical Accessibility to Home Medical Care Using a Claims Database: a Simulation Using Geographical Information System. JMIR aging. 2025. (In press). Ohashi K, Sato M, Fujiwara K, Tanikawa T, Morii Y, Ogasawara K. Spatial accessibility of home visiting nursing: An exploratory ecological study. Health Sci Rep. 2024;7(9):e70078. Luo W, Wang F. Measures of spatial accessibility to health care in a GIS environment: synthesis and a case study in the Chicago region. Environ Plann Plann Des., Shimizu J, Osanai S. Feasibility of home visit by nurses with higher palliative care specialty with visiting nurses in Japan—analysis of nationwide distribution and geographical associations. J Jpn Health Sci. 2014;16: 177–183. [Abstract in English]. Geospatial Information Authority of Japan. Land area. https://www.gsi.go.jp/KOKUJYOHO/OLD-MENCHO-title.htm (Accessed on September 19, 2024) (In Japanese). Nara prefectural government. Estimated population. (August 1, 2024). https://www.pref.nara.jp/6265.htm (Accessed on September 7, 2024) (In Japanese). Geospatial Information Authority of Japan. Digital Land Information Download Website. https://nlftp.mlit.go.jp/ksj/index.html (Accessed on September 7, 2024) (In Japanese). Kinki Welfare Bureau. Lists of designated medical institutions and pharmacies. (as of May 1, 2025). https://kouseikyoku.mhlw.go.jp/kinki/tyousa/shinkishitei.html (Accessed on June 1, 2025) (In Japanese). Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx File name: Additional file 1 File format: Microsoft word Title of data: Information on the three groups of municipalities based on population Description of data: This file includes information on the municipalities included in each group with different population-density (i.e., population per habitable area). Cite Share Download PDF Status: Posted 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-7140322","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508529184,"identity":"6e0329e1-0400-47fb-ab8b-635693dc5cff","order_by":0,"name":"Yasuhiro Morii","email":"","orcid":"","institution":"National Institute Of Public Health, Japan","correspondingAuthor":false,"prefix":"","firstName":"Yasuhiro","middleName":"","lastName":"Morii","suffix":""},{"id":508529185,"identity":"13bf36e2-885f-4f0a-8d78-0cf6bbb4a1a3","order_by":1,"name":"Yasuhiro Nakanishi","email":"","orcid":"","institution":"National Institute Of Public Health, Japan","correspondingAuthor":false,"prefix":"","firstName":"Yasuhiro","middleName":"","lastName":"Nakanishi","suffix":""},{"id":508529186,"identity":"5b4bdd77-8608-4cfb-aded-c53d37253123","order_by":2,"name":"Yuichi Nishioka","email":"","orcid":"","institution":"Nara Medical University, Japan","correspondingAuthor":false,"prefix":"","firstName":"Yuichi","middleName":"","lastName":"Nishioka","suffix":""},{"id":508529187,"identity":"e93915dc-12af-403c-a7ca-7634dfb9ec2a","order_by":3,"name":"Yukio Tsugihashi","email":"","orcid":"","institution":"Nara Medical University, Japan","correspondingAuthor":false,"prefix":"","firstName":"Yukio","middleName":"","lastName":"Tsugihashi","suffix":""},{"id":508529188,"identity":"5dc8f101-500e-47e6-947e-6fad18d8793e","order_by":4,"name":"Tatsuya Noda","email":"","orcid":"","institution":"Nara Medical University, Japan","correspondingAuthor":false,"prefix":"","firstName":"Tatsuya","middleName":"","lastName":"Noda","suffix":""},{"id":508529189,"identity":"b7685dde-dd16-43fc-bb5d-b2bd56fe32d0","order_by":5,"name":"Tomoya Myojin","email":"","orcid":"","institution":"Hamamatsu University School of Medicine, Japan","correspondingAuthor":false,"prefix":"","firstName":"Tomoya","middleName":"","lastName":"Myojin","suffix":""},{"id":508529190,"identity":"5998f218-c490-4896-ad94-065cf6e07819","order_by":6,"name":"Tomoaki Imamura","email":"","orcid":"","institution":"Nara Medical University, Japan","correspondingAuthor":false,"prefix":"","firstName":"Tomoaki","middleName":"","lastName":"Imamura","suffix":""},{"id":508529191,"identity":"f910f680-f8d6-4305-8c8b-9b60e4c7315f","order_by":7,"name":"Manabu Akahane","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACHgY2BOcDgwSDAVSYOC2MM0jWwgxSaEDIXfw9h589+LnDhkG3gfnhZ9sdFnnmDOwXHzDI3MGpReJsm7lh75k0BrMDbMbSuWckii0beIoNGHie4bbmPIOZBG/bYaAWBgPp3DaJxA0HeNIkGHgO49Qhf579m+Tftv9ALeyff1sSo8XgbI+ZNG/bAaAWHjNpRrAW9mN4tRieOVMmLduWzGN2mKfMsrdNotjgMA+zQQIev8idSd8m+bbNTs7sePvmGz/b6vIMjrc/fPCxB3eIwQAPAzOEkQCMHQOGxJ4DBLXAQQIDA/sDBoYfJGgZBaNgFIyC4Q4AmCNRVEOdCugAAAAASUVORK5CYII=","orcid":"","institution":"National Institute Of Public Health, Japan","correspondingAuthor":true,"prefix":"","firstName":"Manabu","middleName":"","lastName":"Akahane","suffix":""}],"badges":[],"createdAt":"2025-07-16 13:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7140322/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7140322/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90466788,"identity":"f49eeca9-0849-4062-8987-ddc7dfab7825","added_by":"auto","created_at":"2025-09-03 05:37:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":306805,"visible":true,"origin":"","legend":"\u003cp\u003eDistributions of municipalities included in each population-density group and of hospitals or clinics providing HMCS\u003c/p\u003e\n\u003cp\u003eFigire 1 shows the distribution of municipalities included in each group based on population density (a) and the distributions of hospitals or clinics providing HMCS services identified by KDB analysis for (b)HMCMF, (c) FAMCMF, and (d) others\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7140322/v1/33c8fb0dc30f696efae2eef6.png"},{"id":90466784,"identity":"7a9e31e4-4217-45d5-ad18-3ab69b76f2d9","added_by":"auto","created_at":"2025-09-03 05:37:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":250631,"visible":true,"origin":"","legend":"\u003cp\u003eThe utilization rates in percentiles by municipalities\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the utilization rates of (a)HMCMF, (b) FAMCMF, and (c) others by municipalities. The rates were presented as percentiles among the 39 municipalities.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7140322/v1/f3a9dcee7cbc676a006b8c65.png"},{"id":90465568,"identity":"d0c6d67d-a70e-41ae-b63b-f5e7a336b4ab","added_by":"auto","created_at":"2025-09-03 05:30:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237960,"visible":true,"origin":"","legend":"\u003cp\u003eThe rate of claims for patients within the same municipalities from the facilities’ perspective\u003c/p\u003e\n\u003cp\u003eFigure legend: Figure 3 shows the rate of claims for patients who lived within the same municipalities from the facilities’ perspective (a) HMCMF, (b) FAMCMF, and (c) others category)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7140322/v1/15a26a2ee12b94f774f8cc6d.png"},{"id":90464883,"identity":"cf261b71-55e4-478a-a894-49c2f2bd884a","added_by":"auto","created_at":"2025-09-03 05:12:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":252984,"visible":true,"origin":"","legend":"\u003cp\u003eThe rate of claims for patients within the same municipalities from the patients’ perspective\u003c/p\u003e\n\u003cp\u003eFigure legend: Figure 4 shows the rate of claims for patients who lived within the same municipalities from the patients’ perspective ((a) HMCMF, (b) FAMCMF, and (c) others category)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7140322/v1/1ccfb7be33a24827754b49c4.png"},{"id":90465579,"identity":"d4bb1abe-9351-4958-806f-709731ddf9a4","added_by":"auto","created_at":"2025-09-03 05:31:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":152294,"visible":true,"origin":"","legend":"\u003cp\u003eTravel distance for the HMCS service categories from the facility perspective\u003c/p\u003e\n\u003cp\u003eFigure 5 illustrates the travel distance for (a) HMCMF, (b) FAMCMF, and (c) others category from the facility perspective.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7140322/v1/bf1984311f3e4cffd0c32b12.png"},{"id":90464892,"identity":"8ada7e41-fef0-4ec8-8ea0-a6d95e889c8d","added_by":"auto","created_at":"2025-09-03 05:12:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":231485,"visible":true,"origin":"","legend":"\u003cp\u003eTravel distances by municipalities\u003c/p\u003e\n\u003cp\u003eFigure 6 illustrates the travel distance for (a) HMCMF, (b) FAMCMF, and (c) others category by municipalities from the facility perspective\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7140322/v1/bd7af461b1c352e7f7e130d5.png"},{"id":90464888,"identity":"b5d6c931-67b9-464f-80f4-a687790b13cd","added_by":"auto","created_at":"2025-09-03 05:12:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":146568,"visible":true,"origin":"","legend":"\u003cp\u003eTravel distance for the HMCS service categories from the patient perspective\u003c/p\u003e\n\u003cp\u003eFigure 7 illustrates the travel distance for (a) HMCMF, (b) FAMCMF, and (c) others category from the patient perspective.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7140322/v1/723b8479b6d45036472abd60.png"},{"id":100370454,"identity":"fa9a4827-d2a6-4f0c-88e0-e6a0ad086128","added_by":"auto","created_at":"2026-01-16 08:05:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2028476,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7140322/v1/86e7aba2-7e51-463a-b7d6-af8063be7fb1.pdf"},{"id":90464874,"identity":"5153411d-388b-416f-a6e9-1cfcb448d406","added_by":"auto","created_at":"2025-09-03 05:12:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19687,"visible":true,"origin":"","legend":"\u003cp\u003eFile name: Additional file 1\u003c/p\u003e\n\u003cp\u003eFile format: Microsoft word\u003c/p\u003e\n\u003cp\u003eTitle of data: Information on the three groups of municipalities based on population\u003c/p\u003e\n\u003cp\u003eDescription of data: This file includes information on the municipalities included in each group with different population-density (i.e., population per habitable area).\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7140322/v1/2f27408664bee63f9f8b903e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analyzing Catchment Areas for Home Medical Care Services Using Real-world Claims Data","fulltext":[{"header":"Background","content":"\u003cp\u003eHome medical care services (HMCS) are of greater importance in countries with aging population such as Japan, which has the highest aging rate in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Medical demands for HMCS will increase with the aging of society in Japan until around 2040 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Japan, it is required that prefectural governments make Regional Medical Plan to ensure that medical services including HMCS are reached to meet needs from local population by optimally allocating healthcare resources [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Also, the prefectures are required to consider regional units that are appropriate in providing HMCS to residents in each area while taking into account resource constraints.\u003c/p\u003e\u003cp\u003eGeographical accessibility is one of the important factors in considering optimal allocation of healthcare resources on HMCS since healthcare providers need to visit patients\u0026rsquo; houses or facilities where patients reside.\u003c/p\u003e\u003cp\u003eFew studies have been conducted on the geographical accessibility to HMCS while there are several studies for other medical services [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. We have previously reported the results of an analysis of the geographical accessibility to HMCS in Nara prefecture of Japan, utilizing the National Health Insurance Database (Kokuho database: KDB), a prefecture-wide database that includes both medical and long-term care claims data [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Using a Geographic Information System (GIS), we demonstrated the applicability of the KDB database for analyzing geographical accessibility by conducting an analysis based on hypothetical patients under the following two scenarios: (1) in which healthcare providers travel to hypothetical patients (randomly distributed according to the distribution of general population aged over 75) from the closest of all clinics or hospitals in Nara prefecture as an ideal case and (2) in which healthcare providers travel to hypothetical patients from the closest of the hospitals or clinics which actually providing HMCS identified from KDB analysis. We clarified a regional disparity in geographical accessibility in a sparsely populated area in the southern part of the prefecture and clarified that in the ideal case where all hospitals or clinics provided the services, the disparity would be improved. In addition to our previous research findings, it is also important to clarify the actual catchment areas where healthcare providers deliver HMCS by using the actual patient locations rather than hypothetical ones, to consider the optimal allocation of supply. It is necessary to demonstrate that such analysis is feasible using KDB database.\u003c/p\u003e\u003cp\u003eConventionally, the balance of supply and demand or needs is evaluated by simple indicators such as supply/demand ratio (e.g., the number of physicians per population). However, these indicators do not take geographical aspects into account. In the Japanese universal healthcare system, HMCS need to be provided within 16 km from hospitals or clinics unless there is a justifying reason [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, hospitals or clinics do not necessarily travel up to 16 km due to limited human resources or operational efficiency. Therefore, when considering optimal allocation of healthcare resources, we need to understand how long healthcare providers practically travel. It is possible that the catchment areas are different among regions and types of services. If we understand the catchment areas, that enables us to perform more analysis of supply and demand with deeper indication such as two-step floating catchment area (2SFCA) methods, which weigh supply using distance decay functions.\u003c/p\u003e\u003cp\u003eThe purpose of this study is to examine analytical methods using a medical claims database (KDB database) to evaluate the supply-demand balance of home medical care services (HMCS) by clarifying the catchment areas. This study, using the real-world database of claims (KDB), will help consider optimal allocations of healthcare resources related to HMCS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eSubjects and scheme\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, we analyzed the KDB, a medical claims database from 39 municipalities in Nara prefecture, Japan, which served as a model area. Information on the geographic locations of the prefecture inside Japan, the secondary areas, and the municipalities was visualized in a previous study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nara prefecture has a population of 1,286,651, a land area of 3,691 km\u003csup\u003e2\u003c/sup\u003e (1% of Japan\u0026rsquo;s land), and an aging rate of 32% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e of the previous study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The southern part (Nanwa secondary medical area) is the most depopulated rural region, and there are also a few depopulated rural municipalities in the eastern part. In this study, the 39 municipalities in Nara were categorized into three groups based on their population densities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], as catchment areas can differ between populated and depopulated areas. The municipalities included in each group are shown in Additional File 1, and the geographical distribution is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-a.\u003c/p\u003e\u003cp\u003eFor each group, the travel distance between hospitals or clinics providing HMCS to patients was analyzed using GIS (Esri, ArcGIS Desktop version 10.8.1) based on the utilization data obtained from KDB analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigire 1 shows the distribution of municipalities included in each group based on population density (a) and the distributions of hospitals or clinics providing HMCS services identified by KDB analysis for (b)HMCMF, (c) FAMCMF, and (d) others\u003c/p\u003e\u003cp\u003e\u003cb\u003eKDB (claims database) analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe information on patients and hospitals or clinics was obtained from an analysis of the healthcare claims in the KDB database of Nara prefecture in FY 2019. The locations of patients aged 75 years or older who lived in Nara prefecture and utilized HMCS in the year were obtained in the form of zip codes. In addition, the institutional codes of hospitals or clinics in Nara from which the patients received HMCS were obtained; the addresses were obtained from a published source [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The utilization or provision of HMCS was defined as having a claim for HMCS [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The aforementioned analysis was performed for three service categories: (1) patients for whom the Home Medical Care Management Fee (HMCMF) was charged; (2) patients for whom the Facility Admission Medical Care Management Fee (FAMCMF) was charged; and (3) and other cases. HMCMF and FAMCMF are applicable when a comprehensive home care plan is created for each patient, regular visits are made for medical examinations, and comprehensive medical management is provided at home and in a long-term care facility respectively by Home Medical Care Support Clinic or Home Medical Care Support Hospital. They are authorized by the Ministry of Health Labour and Welfare (MHLW) with requirements such as 24-hour operation for HMCS and comprehensive management of patients. Home Medical Care Support Clinic or Home Medical Care Support Hospital are expected to play a central role in regional HMCS provision. The utilization rate is defined by the number of claims divided by the population aged 75 years or older. The number of hospitals was aggregated by municipalities for each category. These aggregations were performed using R ver 4.1.2.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeographical Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this analysis, travel distance (i.e., catchment area) from hospitals or clinics that provided HMCS to patients receiving the services (i.e., the travel distance between the actual pair of a hospital or clinic and a patient identified by the KDB analysis) was analyzed.\u003c/p\u003e\u003cp\u003eFirst, the locations of patients based on their zip codes were mapped on GIS as geographically representative points using geocoding function. Next, the locations of hospitals or clinics were mapped based on their addresses using the geocoding function.\u003c/p\u003e\u003cp\u003eThe travel distance was analyzed using the \u0026ldquo;OD cost matrix\u0026rdquo; function; that is, travel distance between every single pair of hospital or clinic and patient was calculated. It is assumed that one claim consisted of one trip. ArcGIS Geo Suite Road Network (ESRI Japan) was used for the analysis.\u003c/p\u003e\u003cp\u003eMedian and percentiles of travel distances were aggregated for the groups by population densities, and the median was also aggregated by municipalities, both from the facilities\u0026rsquo; and patients\u0026rsquo; perspectives, for each category. Catchment areas from the facility perspective indicated how far healthcare providers in the area traveled (from hospitals or clinics) to deliver care; catchment areas from the patient perspective indicatedhow far patients were located from the healthcare providers who delivered HMCS. Catchment areas were calculated for three population density groups (high, middle, and low). In addition, the rates of patients within the same municipalities from the two perspectives were calculated.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe distribution of hospitals or clinics providing HMCS by the service categories is shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-b, -c, and -d. Three small municipalities had no facilities providing any type of HMCS. Additionally, municipalities in the depopulated Nanwa (southern) area and some other small municipalities (villages) in other regions had fewer facilities providing HMCS, particularly HMCMF and FAMCMF.\u003c/p\u003e\u003cp\u003eOverall, in Nara prefecture, 346, 121, and 261 hospitals or clinics and 24,551, 36,764, and 19,665 claims were identified as providing HMCS for HMCMF, FAMCMF, and others, respectively.\u003c/p\u003e\u003cp\u003eThe number of hospitals or clinics providing HMCS services, the number of claims in each category, and utilization rates are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for each population density group. The high-population-density group had higher numbers of hospitals or clinics, claims, while the low-population-density group had lower numbers, particularly for FAMCMF.\u003c/p\u003e\u003cp\u003eThe utilization rates in each municipality are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The utilization rates in Nara overall were 0.101, 0.152, and 0.081 for HMCMF, FAMCMF, and others, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the utilization rates of (a)HMCMF, (b) FAMCMF, and (c) others by municipalities. The rates were presented as percentiles among the 39 municipalities.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAggregated characteristics for the population-density groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003elow density group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emiddle density group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ehigh density group\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e# of hospitals or clinics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHMCMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFAMCMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eothers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e# of claims from facility perspective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHMCMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFAMCMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30782\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eothers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14262\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e# of claims from patient perspective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHMCMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFAMCMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eothers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eUtilitzation rate (claims/population aged 75 years or older)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHMCMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFAMCMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eothers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eTable legend: Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the number of hospitals or clinics, claims, and the utilization rates by types of HMCS services, aggregated from the KDB database analysis\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the rate of claims for patients who lived within the same municipalities from the facilities\u0026rsquo; perspective. The rates were lower for FAMCMF than for other categories. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the rate of claims for patients who lived within the same municipalities from the patients\u0026rsquo; perspective. The rates were lower in the municipalities in the depopulated Nanwa area and some other small municipalities (villages) in other regions.\u003c/p\u003e\n\u003cp\u003eIn Nara prefecture overall, the catchment areas were 2.2 (IQR: 1.1-4.4), 4.6 (IQR: 2.1-8.1), and 3.1 (IQR: 1.3-5.9) km for HMCMF, FAMCMF, and others, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe travel distances in the three groups from the facility perspective are visualized in Figure 5. For HMCMF, the travel distances were 3.4 (IQR: 1.5-5.1), 3.0(IQR: 1.3-6.7), and 2.1(IQR: 1.1-3.8) km in the low-, middle-, and high-population-density groups, respectively (Figure 5-a). For FAMCMF, the travel distances were 8.9 (IQR: 5.3-11.1), 7.6 (IQR: 2.7-14.5), and 4.3 (IQR: 2.0-7.2) km in the low-, middle-, and high-population-density groups, respectively (Figure 5-b). For the others category, the travel distances were 3.8 (IQR: 1.8-8.4), 2.5 (IQR: 1.1-5.6), and 2.8 (IQR: 1.3-6.0) km in the low-, middle-, and high-population-density groups, respectively (Figure 5-c). Figure 6 shows the travel distances from the facilities\u0026rsquo; perspective by municipalities. The travel distances were basically not much different among the groups or municipalities.\u003c/p\u003e\n\u003cp\u003eThe travel distances in the three groups from the patients\u0026rsquo; perspective are visualized in Figure 7. For HMCMF, the travel distances were 9.0 (IQR: 2.6-13.7), 2.3 (IQR: 1.1-4.7), and 2.1 (IQR: 1.1-4.0) km in the low-, middle-, and high-population-density groups, respectively (Figure 7-a). For FAMCMF, the travel distances were 13.5 (IQR: 8.9-23.2), 6.6 (IQR: 2.7-14.1), and 4.1 (IQR: 1.9-7.1) km in the low-, middle-, and high-population-density groups, respectively (Figure 7-b). For the others category, the travel distances were 5.7 (IQR: 1.8-8.4), 2.6 (IQR: 1.1-6.5), and 2.6 (IQR: 1.3-5.7) km in the low-, middle-, and high-population-density groups, respectively (Figure 7-c).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed the catchment areas of HMCS in Nara prefecture, Japan, as a model case, using actual patient location data obtained from the KDB database and GIS. By using KDB, a real-world database, we were able to perform catchment area analysis reflecting actual practices, which is crucial for determining the optimal allocation of HMCS-related healthcare resources.\u003c/p\u003e\n\u003cp\u003eFirst, regarding HMCS supply, the results found that there was an inadequate number of hospitals and clinics providing HMCS in municipalities in the depopulated Nanwa area and some other small municipalities (villages) in other regions, particularly for HMCMF and FAMCMF. These fees apply to facilities authorized as Home Medical Care Support Clinics or Home Medical Care Support Hospitals that provide HMCS services regularly with comprehensive medical management. The results indicate that such facilities for HMCS, which can provide comprehensive medical management regularly, are scarce in these rural areas. The utilization rates were particularly lower in these rural areas for these categories. The disparity in the distribution of facilities could have led to the difference in the utilization rates. It has been previously reported that utilization rates of HMCS services overall were lower in these areas [8]. Adding to the research, our results clarified that the disparity was larger for HMCS with HMCMF and FAMCMF, which are considered comprehensive medical care services. MHLW reported that increasing the supply (i.e., the number of facilities providing HMCS) is a key issue in promoting the utilization of HMCS. In Nara prefecture, the issue could apply to rural areas, particularly for HMCMF and FAMCMF. A supply-demand balance for FAMCMF will also be of great importance in achieving a more integrated medical and long-term care.\u003c/p\u003e\n\u003cp\u003eThe travel distances for FAMCMF were longer than for the other two categories. A possible explanation is that more than one patient can be treated at a long-term care facility for FAMCMF; this is efficient for HMCS providers, even though travel time is relatively long. Further, HMCMF and the others category are mostly provided on an individual need basis. For FAMCMF, some patients were provided with the service from clinics or hospitals more than 16 km away.\u003c/p\u003e\n\u003cp\u003eThe catchment areas for HMCMF and the others category were not much different between the high-, middle-, and low-population-density groups; however, the interquartile ranges were narrower in the high-population-density group. It is understandable that, considering operative efficiency, catchment areas do not differ much on average, regardless of the area. The catchment areas for FAMCMF were smaller in the high population density group. Overall, in more densely populated areas, healthcare providers could cover demands within shorter distances.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom the patients\u0026rsquo; perspective, the travel distance was longer, or there were no patients in the rural municipalities in the depopulated Nanwa area and some other small municipalities (villages) in other regions. The utilization rates were lower in these municipalities. These results indicate that, in these municipalities, the needs for HMCS are unlikely to be met. In addition, the results showed that even when HMCS services were provided, those services were provided by facilities in other municipalities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe prefectures in Japan are required to consider appropriate regional units to ensure that HMCS services meet the needs of residents. The working group of HMCS in the Ministry of Health, Labour and Health stated that the regional units are basically municipalities, while units will be larger depending on available healthcare resources [3]. Our results can inform the establishment of optimal and practical regional units, given that current resource constraints make it difficult to ensure adequate supply in every municipality.\u003c/p\u003e\n\u003cp\u003eIn this study, the catchment areas were different between the categories, but not very much different among the population density groups. In general, the further a patient is from a facility, the less likely the facility is to provide services to the patient. Therefore, it is essential to take into account distance when comparing healthcare needs and supply (i.e., a simple indicator such as the supply-demand ratio is not always applicable, particularly to services requiring travel, such as HMCS). For example, the 2SFCA method weighs the supply using distance decay functions [9-10], which helps measure supply-demand ratios that reflect \u0026ldquo;real-life\u0026rdquo; healthcare behaviors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of catchment areas obtained in this research can be applied to evaluate the sufficiency of HMCS resources while considering travel. For example, the results could inform the estimation of a distance decay function when the 2SFCA method analysis is performed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study methodology is universal and can be applied to other areas or countries, as well as to other fields of healthcare, when considering geographical accessibility and catchment areas. However, this study has several limitations. First, due to limited data, travel from one patient\u0026rsquo;s home to another was not considered. Second, this study only used KDB data from FY2019, which could cause some uncertainty in the results, particularly in smaller municipalities. Third, the patient address in claims does not guarantee that it is the place where the patient actually lives (e.g., it is possible that a patient enters a long-term care facility in municipality B, while his/her registered domicile remains at municipality A); although the rate is expected to be low. Future research should create algorithms to identify the discrepancy between the actual addresses and the registered domicile. Further, our analysis targeted Nara prefecture, and therefore, hospitals and clinics outside the prefecture were not considered. Lastly, while this study divided the subject municipalities into three groups based on population density, it is still not clear whether the results could be applied to metropolises since Nara prefecture does not have such cities. Likewise, the results for areas with low population density may not fully reflect the conditions in highly depopulated areas, as depopulated municipalities in Nara prefectures had limited numbers of hospitals or clinics providing and patients receiving HMCS services. Further research is required to clarify the catchment areas in metropolitan areas and highly depopulated areas.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study analyzed real-world catchment areas\u0026mdash;defined as travel distances from hospitals or clinics to patients\u0026mdash;for three HMCS service categories, using the KDB database of Nara prefecture. The results clarified that the catchment areas were larger for FAMCMF services than for other categories. From the facility perspective, the catchment areas were not much different between the population density groups. The facilities for HMCMF and FAMCMF were scarce, and travel distances from the patient perspective were longer in municipalities in Nanwa and some other rural municipalities. Further, the utilization rates for these services were lower in these areas, and their services were provided by other municipalities. Therefore, the study concludes that, the need for these services, particularly HMCMF and FAMCMF, was likely unmet.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHMCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHome medical care services\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGeographical information system\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHMCMF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHome Medical Care Management Fee\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFAMCMF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFacility Admission Medical Care Management Fee\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e2SFCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTwo-step floating catchment area\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMHLW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMinistry of Health Labour and Welfare\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee in National Institute of Public Health, Japan（NIPH-IBRA#12324-2）. The Requirement for informed consent was waived because all the data were anonymized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was partially supported by a Grant-in-Aid for Scientific Research from the Ministry of Health, Labour and Welfare, Japan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYM, YN, and MA designed the research plan; YT, TN, TM, and TI contributed significantly to the development of the research. YM performed the geographical analysis, and YN performed the claims database analysis. AM led the research project. YM drafted the manuscript, and all authors reviewed it. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCabinet Office. The current situation of aging of society. (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www8.cao.go.jp/kourei/whitepaper/w-2023/html/zenbun/s1_1_2.html\u003c/span\u003e\u003cspan address=\"https://www8.cao.go.jp/kourei/whitepaper/w-2023/html/zenbun/s1_1_2.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on September 7, 2024) (In Japanese).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe Ministry of Health, Labour and Welfare. Constructing the structure of providing HMCS (May 24, 2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000194369.html\u003c/span\u003e\u003cspan address=\"https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000194369.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on September 7, 2024) (In Japanese).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe Ministry of Health, Labour and Welfare. HMCS -toward 8th regional medical plan. (October 13, 2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mhlw.go.jp/stf/newpage_24354.html\u003c/span\u003e\u003cspan address=\"https://www.mhlw.go.jp/stf/newpage_24354.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on September 7, 2024) (In Japanese).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe Ministry of Health, Labour and Welfare. Regional Medical Plan. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/iryou/iryou_keikaku/index.html\u003c/span\u003e\u003cspan address=\"https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/kenkou_iryou/iryou/iryou_keikaku/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on September 7, 2024) (In Japanese).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchwarz J, Hemmerling J, Kabisch N, Galbusera L, Heinze M, von Peter S, et al. Equal access to outreach mental health care? Exploring how the place of residence influences the use of intensive home treatment in a rural catchment area in Germany. BMC Psychiatry. 2022;22(1):826.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Zhang Q, Spatz ES, Gao Y, Eckenrode S, Johnson F, et al. Persistent geographic variations in availability and quality of nursing home care in the United States: 1996 to 2016. BMC Geriatr. 2019;19(1):103.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNaruse T, Matsumoto H, Fujisaki-Sakai M, Nagata S. Measurement of special access to home visit nursing services among Japanese disabled older adults: using GIS and claim data. BMC Health Serv Res. 2017;17(1):377.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorii Y, Nakanishi Y, Nishioka Y, Tsugihashi Y, Noda T, Myojin T et al. Analyzing Disparity in Geographical Accessibility to Home Medical Care Using a Claims Database: a Simulation Using Geographical Information System. JMIR aging. 2025. (In press).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhashi K, Sato M, Fujiwara K, Tanikawa T, Morii Y, Ogasawara K. Spatial accessibility of home visiting nursing: An exploratory ecological study. Health Sci Rep. 2024;7(9):e70078.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo W, Wang F. Measures of spatial accessibility to health care in a GIS environment: synthesis and a case study in the Chicago region. Environ Plann Plann Des., Shimizu J, Osanai S. Feasibility of home visit by nurses with higher palliative care specialty with visiting nurses in Japan\u0026mdash;analysis of nationwide distribution and geographical associations. J Jpn Health Sci. 2014;16: 177\u0026ndash;183. [Abstract in English].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeospatial Information Authority of Japan. Land area. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsi.go.jp/KOKUJYOHO/OLD-MENCHO-title.htm\u003c/span\u003e\u003cspan address=\"https://www.gsi.go.jp/KOKUJYOHO/OLD-MENCHO-title.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on September 19, 2024) (In Japanese).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNara prefectural government. Estimated population. (August 1, 2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pref.nara.jp/6265.htm\u003c/span\u003e\u003cspan address=\"https://www.pref.nara.jp/6265.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on September 7, 2024) (In Japanese).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeospatial Information Authority of Japan. Digital Land Information Download Website. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nlftp.mlit.go.jp/ksj/index.html\u003c/span\u003e\u003cspan address=\"https://nlftp.mlit.go.jp/ksj/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on September 7, 2024) (In Japanese).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKinki Welfare Bureau. Lists of designated medical institutions and pharmacies. (as of May 1, 2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kouseikyoku.mhlw.go.jp/kinki/tyousa/shinkishitei.html\u003c/span\u003e\u003cspan address=\"https://kouseikyoku.mhlw.go.jp/kinki/tyousa/shinkishitei.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on June 1, 2025) (In Japanese).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Home medical care, Geographical accessibility, Geographical information system, Medical claims database, Catchment area","lastPublishedDoi":"10.21203/rs.3.rs-7140322/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7140322/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWhen evaluating the supply-demand balance of home medical care services (HMCS), it is important to consider catchment areas, as the distance to patients may influence healthcare providers\u0026rsquo; behavior. This study aims to examine analytical methods using a medical claims database to identify catchment areas for HMCS, to better assess their supply-demand balance.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe subjects were 39 municipalities in Nara prefecture, Japan, as a model case. The information on patients and hospitals or clinics was obtained from medical claims in the prefecture-wide (KDB) database in FY 2019. The travel distances from hospitals or clinics to patients were analyzed using a geographical information system (GIS) for three categories of HMCS: (1) cases where the Home Medical Care Management Fee (HMCMF) was charged, (2) cases where the Facility Admission Medical Care Management Fee (FAMCMF) was charged, and (3) all other cases. The catchment areas were aggregated for three groups based on population density, from both the facility and patient perspectives. In addition, utilization rates and the number of hospitals or clinics providing HMCS were also aggregated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe number of facilities for HMCMF and FAMCMF was limited, and the utilization rates for these services were lower in sparsely populated municipalities such as Nanwa in the southern part. From the facility perspective, the catchment areas were 3.4, 3.1, and 2.1 km for HMCMF, 8.9, 7.6, and 4.3 km for FAMCMF, and 3.8, 2.5, and 2.8 km for the other category in the low, middle, and high population density groups, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe catchment areas for HMCS were analyzed using the KDB database and GIS. In Nara Prefecture, the catchment areas were broader for FAMCMF than for the other categories. The catchment areas did not differ much between the population density groups from the facility perspective. The results also indicate that, particularly for HMCMF and FAMCMF, the needs in some sparsely populated municipalities may not be adequately met.\u003c/p\u003e","manuscriptTitle":"Analyzing Catchment Areas for Home Medical Care Services Using Real-world Claims Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 05:12:07","doi":"10.21203/rs.3.rs-7140322/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"248ab227-cc22-4477-a1b6-520fd56b5d71","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-14T12:25:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 05:12:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7140322","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7140322","identity":"rs-7140322","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}