Background: When analyzing regional disparities in healthcare resources, hospital accessibility is given little consideration. We surveyed accessibility from residential districts to medical institutions using GIS (Geographic Information System) and estimated Gini coefficient for each hospital distribution.
Methods: The subjects were 2,688 census mesh blocks ( “Cho-cho-aza” ) and 109 hospitals in Tochigi prefecture. The number of hospitals located within the road distances of 5 km, 10 km and 15 km from the geometrical center of each block was calculated using GIS. The Gini coefficient of each hospital per 100 residents was calculated among the regions located within 5 km, 10 km and 15 km from the geometrical center of the census mesh block.
Results: The population of each block was 748±1,067 (mean±SD), and the road distance to the nearest hospital from the center of each block was 4.3±4.5 km. The number of census mesh blocks with distances from the center of each block to the nearest hospital within 5 km, 5-10 km, 10-15 km and more than 15 km were 1909 (71.0%), 561 (20.9%), 139 (5.2%) and 79 (2.9%) respectively. The number of hospitals located within 5 km, 10 km and 15 km were 3.3±4.7, 8.3±8.6 and 14.4±11.4. Gini coefficients were 0.65, 0.52 and 0.43.
Conclusion: When analyzing regional disparities in healthcare resources, it is necessary to take into account not only the number of physicians and beds, but also accessibility. Gini coefficient is useful to estimate geographical distributions, and can be used as an indicator for improvement projects for hospitals.

Introduction : The appropriate size of the regional coverage area for primary care in Japan has been unclear. The aim of this study was to determine the geographical distribution of primary care clinics for elderly ambulatory diabetic patients.
Methods : Using an insurance claims database, we extracted data of patients aged 75 years and older requiring ambulatory diabetic care in May 2010 in Ibaraki prefecture. The geographical distance from each municipal office to the clinics was analyzed.
Results : A total of 17,717 data points were extracted from the database. Data points that could not be mapped due to coding errors were eliminated, resulting in 17,144 (96.8%) data points that were ultimately analyzed. The median [25th-75th percentile] geographical distance was 5.5 [2.3-9.9] km. The distance was not related to municipal population, aging rate, or area size.
Conclusion : The coverage area for diabetic care in this primary care setting was estimated. For most elderly ambulatory diabetic patients, clinics are distributed within a 10-km radius area. Further investigation is needed to clarify primary care coverage areas that result in the most efficient use of medical resources.