Background: A decrease in populations could affect healthcare access and systems, particularly in medically underserved areas (MUAs) where depopulation is becoming more prevalent. This study aimed to simulate the future population and land areas of MUAs in Japan. Methods: This study covered 380,948 1 km meshes, 87,942 clinics, and 8354 hospitals throughout Japan as of 2020. The areas outside a 4 km radius of medical institutions were considered as MUAs, based on the measure of areas in the current Japanese Medical Care Act. Based on the population estimate for a 1 km mesh, the population of mesh numbers of MUAs was predicted for every 10 years from 2020 to 2050 using geographic information system analysis. If the population within a 4 km radius from a medical institution fell below 1000, the institution was operationally assumed to be closed. Results: The number of MUAs was predicted to decrease from 964,310 (0.77% of the total Japanese population) in 2020 to 763,410 (0.75%) by 2050. By 2050, 48,105 meshes (13% of the total meshes in Japan) were predicted to be new MUAs, indicating a 31% increase in MUAs from 2020 to 2050. By 2050, 1601 medical institutions were tentatively estimated to be in close proximity. Conclusions: In Japan, the population of MUAs will decrease, while the land area of MUAs will increase. Such changes may reform rural healthcare policy and systems.

Many small and midsized cities around the world are expected to experience rapid shrinking and aging of their populations in the near future. In Japan, these dramatic demographic shifts have already begun in all but the largest cities, creating an urgent need to redesign public transportation systems to accommodate the transit needs of smaller, older populations. Here we focus on the specific case of Takamatsu, a medium-sized city with a population of 420,000 that is currently redesigning its transit system to better serve an aging city with a declining working-age population. We use the agent-based transportation simulation model MATSim to predict the ramifications of Takamatsu’s transit system reforms on transportation behavior in the year 2050. Our analysis reveals how the effects of Takamatsu’s transit reforms vary geographically and temporally, with societal implications—particularly for the mobility of elderly residents—that we discuss.

Background: Geographical imbalances in the health workforce, particularly the shortage of health care workers in rural areas, is an issue of social and political concern in most countries. Estimating the number of required doctors is essential for evidence-based health policy planning. In this study, we propose two methods for estimating the number of required doctors using a simple method. One is counting by unit and the other is incorporating access to medical institutions. The purpose of this study is to verify the need to incorporate access to medical institutions when estimating the number of required physicians in a region by comparing both estimation methods from the viewpoint of regional population density. Methods: We calculated the ratio of outpatients who can access medical institutions and the number of required physicians using the travel time by car and the number of patients who can be treated per doctor per day (estimation method for the number of physicians based on the access simulation, hereinafter referred to as EAS). We compared the results of this estimation with those of a conventional method, such as the number of doctors per population (estimation method for the number of physicians based on the number of patients, hereinafter referred to as ENP) to show how important it is to incorporate the element of accessibility in such a simulation analysis. Based on the results, we discussed the applicability of the proposed method. Results: ENP estimated that 38,685 outpatient primary care (PC) physicians were required and EAS estimated that 46,378 were required. There was a difference of about 8000. A comparison of the EAS-estimated number of physicians and the ENP-estimated number of physicians showed that the ENP-estimated number was small, particularly in areas with low population density. Conclusions: The results showed that it is effective to use the proposed EAS method for the estimation of PC physicians, particularly in areas with low population density. We showed that the method of allocating the number of physicians in proportion to the number of patients in a certain unit requires paying attention to the setting of the unit.

The purpose of this study is to analyze the optimum location of fire departments. The idea of this research is to minimize the response time by confirming emergency services dispatched and absence situations. The method of analysis is the calculation of average response times using the optimization algorithm containing a dispatch simulation. We calculated the results by using actual past dispatch data. The results show that the median model shortened traveling distances by about 367m and the simulation model shortened distances by about 438m as compared with the current location.