Aging speed differs among regions resulting in regional differences of the medical demand in Japan.　To calculate the social burden of each region of a specified disease would contribute to health policy making.　In this study, we developed the method to calculate the social burden of disease of each prefecture using Cost of Illness （COI） method and governmental statistics.

　Cerebrovascular disease （ICD-10:I60-I69） was selected as an object of investigation.　COI of cerebrovascular disease was about 22,503 JPY per capita, the maximum was 34,132 JPY in Kagoshima and the minimum was 16,037 JPY in Shiga.　The differences in composition of the three components of COI were also observed.

　In addition, we examined whether social factors were related to COI of cerebrovascular disease using multiple regression analysis.　Each component of COI was related to social factors such as the average length of stay （LOS） and aging rate.　It was suggested that if LOS was reduced to 5/6, the direct costs might be reduced by 16.2%

　Regional differences in admission rate were neither disclosed nor studied in the field of intensive care.　The aim of this study is to clarify actual differences in admission rate and completion proportion of intensive care for elderly patients within each secondary healthcare area in Fukuoka prefecture.　Moreover, we evaluated validity of current hospitalization rate and suitable demand of secondary healthcare area related to intensive care.　Using healthcare claims data of the Fukuoka Late Elders’ Health Insurance in fiscal year 2016, we created database related to admission to ICU.　We calculated admission rate and completion proportion by each residential area.　These indices were also calculated by postoperative and non-operative cases.　The admission rate ranged from 4.2 to 18.5.　There were large differences in postoperative admission rate ranging from 3.0 to 10.0 and in non-operative admission rate ranging from 1.2 to 9.3.　The completion proportion could be improved in most areas over 90% by reconstruction of secondary healthcare area, which was proposed in previous study.　However, there were area under 50% of completion proportion because of lack of intensive care facilities.　Our study revealed that there were regional differences in admission rate related to intensive care.　Furthermore, improvements in completion proportion of intensive care by reconstruction of secondary healthcare area was different from that of previous study.　Our findings suggest that the area differences in intensive care might be affected by supplier induced demand as well as by the distribution of resources for intensive care.

　An automatic calculation algorithm that can calculate the contribution margin （gross margin） for each case and each procedure （surgery/anesthesia） using data from the supply, processing and distribution （SPD）, and anesthesia information systems was developed and introduced in 2010.　Costs of materials not considered as consumables for individual patients were divided into 3 categories in the material master and allocated to each case in a manner proportional to the surgical and anesthesia fees.　As a result of using the algorithm for 8 years, the contribution margin could be calculated for all 41,067 cases, and annual changes in the contribution margin in each surgical department and in the Department of Anesthesiology indicated the potential for long-term use of the algorithm.　In addition, it was shown that not only medical, but also economic aspects can be included in case statistics.　Furthermore, the contribution margin of surgery for each department, contribution margin of anesthesia, the material consumption costs, contribution margin ratio, and other items were also shown to be useful for cost management.

　As a result of the aggregation of consumable material costs, costs specific to each patient accounted for 86% and costs not specific to each patient, but were allocated to each case and accounted for the remaining 14%, consisted of the surgery costs （61%）, anesthesia costs （15%） and those common to surgical departments （24%）.　On the whole, the algorithm allowed clearer visualization of the medical and economic aspects of surgical departments.