{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T22:49:54Z","timestamp":1791586194544,"version":"4.3.4"},"reference-count":25,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Across Japan, there are \u201cnon-physician areas\u201d where medical institutions are not present and health care access is difficult. Although these areas are defined as a single administrative category, they may vary according to regional demographic characteristics. Understanding this possible heterogeneity may be important for developing more context-sensitive strategies to improve health care accessibility.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study examined whether non-physician areas can be classified based on regional demographic characteristics using an unsupervised machine learning model.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n A total of 590 non-physician areas were identified from the national survey conducted by the Ministry of Health, Labour and Welfare of Japan (2019). Data on regional demographic characteristics were also obtained. After\n z<\/jats:italic>\n score standardization, k-means clustering was used for classification. The optimal number of clusters was determined using silhouette scores. The data on non-physician areas were divided into training (354\/590, 60.0% areas) and validation (236\/590, 40.0% areas) datasets. The reproducibility of the cluster structure was evaluated using Jensen-Shannon distance and chi-square tests, and principal component analysis (PCA) was used to examine the overall structure of the clusters.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The optimal number of clusters was 3 (silhouette score=0.266), indicating that non-physician areas could be classified into 3 types. The cluster distributions of the training and validation datasets were consistent (Jensen-Shannon distance=0.017;\n P<\/jats:italic>\n =.76), supporting the reproducibility of the cluster structure. The PCA suggested that differences among non-physician areas could be interpreted along 2 axes, including population age structure and settlement size. The 3 clusters represented younger, intermediate, and older types, characterized by differences in demographic and settlement characteristics.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The present study demonstrated that administratively defined non-physician areas in Japan are not uniform and can be classified into 3 demographically distinct types. The typology may provide a framework for understanding differences not captured by the current administrative designation and for considering more context-sensitive health care strategies.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/101570","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T22:11:11Z","timestamp":1791583871000},"page":"e101570-e101570","source":"Crossref","is-referenced-by-count":0,"title":["Non-Physician Areas in Japan: Typological Classification Based on Regional Demographic Characteristics"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4978-8652","authenticated-orcid":false,"given":"Takashi","family":"Kuwayama","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8119-633X","authenticated-orcid":false,"given":"Kazuhiko","family":"Kotani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"issue":"1","key":"R1","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-025-12848-w","article-title":"Rural and urban disparities in access and quality of healthcare in the Japanese healthcare system: a scoping review","volume":"25","author":"Kaneko","journal-title":"BMC Health Serv Res"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1186\/1475-9276-12-18","article-title":"Patient-centred access to health care: conceptualising access at the interface of health systems and populations","volume":"12","author":"Levesque","journal-title":"Int J Equity Health"},{"issue":"5","key":"R3","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1007\/s10900-013-9681-1","article-title":"Traveling towards disease: transportation barriers to health care access","volume":"38","author":"Syed","journal-title":"J Community Health"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.34172\/ijhpm.2023.7454","article-title":"Characteristics of medical deserts and approaches to mitigate their health workforce issues: a scoping review of empirical studies in Western countries","volume":"12","author":"Flinterman","journal-title":"Int J Health Policy Manag"},{"key":"R5","unstructured":"Annual health, labour and welfare report 2022. 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