[Examination of Factors Affecting Life Expectancy by Prefecture Based on the Criteria of the Organisation for Economic Co-operation and Development].

OBJECTIVES: We investigated the indicators affecting life expectancy at birth and life expectancy at age 65 by multiple regression analysis and principal component analysis, and examined the factors affecting the longevity. METHODS: We set indicators for health status, risk factors, access to care, quality of care and health care resources. Then, we conducted multiple regression analysis with life expectancy at birth and life expectancy at age 65 as the objective variables and 22 indicators as explanatory variables. Principal component analysis was also performed on the 22 indicators. RESULTS: Men's life expectancy at birth was positively affected by hospital admission ratio and national health insurance costs, and negatively by the rate of requirement of care certification and alcohol consumption. Men's life expectancy at age 65 was positively affected by income-to-medical expenses ratio and hospitalization treatment ratio, and negatively by requiring care certification rate, smoking rate and obesity rate. Women's life expectancy at birth was positively affected by population coverage and hospitalization treatment ratio, and negatively by women's heart disease mortality rate, requiring care certification rate and smoking rate. Women's life expectancy at age 65 was positively affected by late-stage elderly medical costs and the number of doctors, and negatively by requiring care certification rate and air pollution. Principal component 1 indicated "aging high-medical-resource society", principal component 2 indicated "high mortality from heart disease", and principal component 3 indicated the "degree of risk factor". CONCLUSIONS: On the basis of the indicators found to affect life expectancy at birth and life expectancy at age 65, it is necessary to take measures to ensure a long life.

[Evaluation of the Performance of Health Systems by Prefecture].

OBJECTIVES: To clarify the performance of health systems (performance), the state of prefectures was investigated. METHODS: Using the method developed by the Organisation for Economic Co-operation and Development, we conducted the study using 27 indicators. To evaluate the performance, we examined the signal-to-noise ratio of transcription (η) by integrating indicators using the Mahalanobis-Taguchi method. The η was separated by a median. The ratio of prefectures in East or West Japan and the presence or absence of ordinance-designated cities were studied using the chi-square test. Furthermore, the correlation among the total component η (T-η), each component η, and expenditures, and the correlation between T-η and life expectancy, were studied. RESULTS: The T-η positively correlated with the risk factors (RF)-η and the resources (R)-η, and negatively correlated with the health status (HS)-η. The number pf prefectures without ordinance-designated cities where the T-η and RF-η exceeded the median was large. The number of prefectures with ordinance-designated cities where the HS-η exceeded the median was also large. In addition, there were many prefectures where the T-η was in the top 25% in West Japan. There were positive correlations between the total expenditures and the T-η, the expenditures and the RF-η, and the expenditures and the R-η. There was a negative correlation between the expenditures and the HS-η, and the T-η and the life expectancy. CONCLUSIONS: For life expectancy, prefectures with too good performance were recognized. In these prefectures, correction of resources is necessary.

Examination of Factors that Influence the Operation Income and Expenditure Balance Difference Rate of 20 Educational Foundation Universities.

OBJECTIVES: The objective of this study is to examine the factors that influence the operation income and expenditure balance ratio of school corporations running university hospitals by multiple regression analysis. METHODS: 1. We conducted cluster analysis of the financial ratio and classified the school corporations into those running colleges and universities.2. We conducted multiple regression analysis using the operation income and expenditure balance ratio of the colleges as the variables and the Diagnosis Procedure Combination data as the explaining variables.3. The predictive expression was used for multiple regression analysis. RESULTS: 1. The school corporations were divided into those running universities (7), colleges (20) and others. The medical income ratio and the debt ratio were high and the student payment ratio was low in the colleges.2. The numbers of emergency care hospitalizations, operations, radiation therapies, and ambulance conveyances, and the complexity index had a positive influence on the operation income and expenditure balance ratio. On the other hand, the number of general anesthesia procedures, the cover rate index, and the emergency care index had a negative influence.3. The predictive expression was as follows.Operation income and expenditure balance ratio = 0.027 × number of emergency care hospitalizations + 0.005 × number of operations + 0.019 × number of radiation therapies + 0.007 × number of ambulance conveyances - 0.003 × number of general anesthesia procedures + 648.344 × complexity index - 5877.210 × cover rate index - 2746.415 × emergency care index - 38.647Conclusion: In colleges, the number of emergency care hospitalizations, the number of operations, the number of radiation therapies, and the number of ambulance conveyances and the complexity index were factors for gaining ordinary profit.

Type Classification Based on Inpatient Ratio According to Major Diagnosis Category of Group III in the Diagnosis Procedure Combination System.

OBJECTIVES: The target was 1,283 hospitals in group III of medical institutions. Using the Diagnosis Procedure Combination (DPC) data and the inpatient ratio according to the major diagnosis category (MDC), group III hospitals were clarified on the basis of the Mahalanobis distance (D(2)) calculated using the Mahalanobis·Taguchi (MT) method. METHODS: About 50 hospitals with the top inpatient ratio in each MDC and 1,233 other hospitals. The following were examined. 1) Significant difference between DPC data and inpatient ratio. 2) Distribution of D(2). 3) Classification based on the distribution of D(2). RESULTS: The top hospital was small scale, and there were few cases of emergency hospitalization, operation, general anesthesia induction, and chemotherapy. The number of cases involving the respiratory system, circulatory system, digestive and hepatobiliary systems, metabolic system, muscloskeletal system, kidney and urinary tracts was high with an inpatient ratio of more than 90%. On the bases of the distribution of D(2), the hospitals were classified into four types. Group one consisted of 213 special hospitals that showed high inpatient ratios of cases involving the nervous system, respiratory system, digestive and hepatobiliary systems, muscloskeletal system, injuries and burns. Group two consisted of 195 associate special hospitals that showed high inpatient ratios of cases involving the circulatory organ, metabolic system, kidney and urinary tracts, pediatric diseases, and mental diseases. Group three consisted of 223 hospitals for the seven remaining diseases. Group four consisted of 652 hospitals showing other distributions of D(2). CONCLUSION: Classification of hospitals to four types was possible by the MT method depending on the medical treatment results.

[Examination of the Difference in Medical Treatment Contents According to Major Diagnostic Category of Hospital Group I and Group II Using the Diagnosis Procedure Combination Survey Data].

OBJECTIVES: A difference in the medical treatment situation between the first group and the second group of the hospital group in the DPC system was clarified using Diagnosis Procedure Combination (DPC) survey data according to Major Diagnostic Category (MDC). Furthermore, the division between the first group and the second group was examined. METHODS: DPC survey data collected in 2012 was used. According to MDC, significant differences in the patient ratio of hospitalization, the number of planned hospitalizations, the number of emergency hospitalizations, the number of ambulance conveyances, and the number of treatments were considered. Then, by the Mahalanobis-Taguchi method, distributions of the Mahalanobis distance and item choice according to MDC were considered. RESULTS: Many items according to MDC showed significant differences between the first group and the second group. The Mahalanobis distance was increased by MDC 16 disease when divided by the Mahalanobis distance of 1.0 between the first group and the second group. The item, which contributed to the calculation of the Mahalanobis distance by item choice, varied and showed a difference between the first group and the second group. CONCLUSIONS: The second group was authorized by the hospital followed by the first group. However, the results showed significant differences in the number of DPC survey data and the Mahalanobis distance of many items.

[Examination of diagnosis procedure combination survey data that influence function evaluation coefficient II].

OBJECTIVES: On the basis of Diagnosis Procedure Combination (DPC) survey data, the factors that increase the value of function evaluation coefficient II were considered. METHODS: A total of 1,505 hospitals were divided into groups I, II, and III, and the following items were considered. 1. Significant differences in function evaluation coefficient II and DPC survey data. 2. Examination of using the Mahalanobis-Taguchi (MT) method. 3. Correlation between function evaluation coefficient II and each DPC survey data item. RESULTS: 1. Function evaluation coefficient II was highest in group II. Group I hospitals showed the highest bed capacity, and numbers of hospitalization days, operations, chemotherapies, radiotherapies and general anesthesia procedures. 2. Using the MT method, we found that the number of ambulance conveyances was effective factor in group I hospitals, the number of general anesthesia procedures was effective factor in group II hospitals, and the bed capacity was effective factor in group III hospitals. 3. In group I hospitals, function evaluation coefficient II significantly correlated to the numbers of ambulance conveyances and chemotherapies. In group II hospitals, function evaluation coefficient II significantly correlated to bed capacity, the numbers of ambulance conveyances, hospitalization days, operations, general anesthesia procedures, and mean hospitalization days. In group III hospitals, function evaluation coefficient II significantly correlated to all items. CONCLUSIONS: The factors that improve the value of function evaluation coefficient II were the increases in the numbers of ambulance conveyances, chemotherapies and radiotherapies in group I hospitals, increases in the numbers of hospitalization days, operations, ambulance conveyances and general anesthesia procedures in group II hospitals, and increases in the numbers of hospitalization days, operations and ambulance conveyances. These results indicate that the profit of a hospital will increase, which will lead to medical services of good quality.

Examination of correlation between medical expenses with average life expectancy according to municipality and gender.

OBJECTIVES: To determine the influence of medical expenses on life expectancy. METHODS: The expenses of 1,718 municipalities were divided into total expenses, hospitalization expenses and expenses other than hospitalization and dental expenses. 1) The correlation of life expectancy with sex was considered. 2) The correlation between expenses and life expectancy was considered. 3) The correlation of life expectancy or expenses with the numbers of doctors, dentists, facilities and beds was considered. 4) Using the Mahalanobis-Taguchi method, a unit space was formed by 10 municipalities with a high life expectancy, and D(2) was calculated. When D(2) was outside the unit space, the expenses were not as much as those of the 10 municipalities with a high life expectancy. RESULTS: 1) Life expectancy showed a positive correlation with gender. 2) Male life expectancy showed a negative correlation with total and hospitalization expenses, and a positive correlation with dental expenses. A positive correlation was found between each of expenses and female life expectancy. Total expenses, hospitalization expenses and expenses other than those on hospitalization showed a negative correlations with life expectancy in Hokkaido. Dental expenses showed a negative correlation with life expectancy in Chubu, hospitalization expenses showed a negative correlation with life expectancy in Kyushu. Total, hospitalization and dental expenses showed positive correlations with life expectancy in Tohoku, and dental expenses showed a positive correlation with life expectancy in Kanto and Chubu. 3) Total expenses, hospitalization expenses and expenses other than those on hospitalization were found to correlate with the number of doctors. Dental expenses were found to correlate with the numbers of doctors, facilities, and beds. 4) The difference in among estranged municipalities was considered. Life expectancy was significantly short in estranged municipalities, and the total expenses and hospitalization expenses were large. CONCLUSIONS: The relationship of medical expenses with life expectancy became clear. It was assumed that medical performance was poor in estranged municipalities.

[Diagnosis of liver diseases by classification of laboratory signal factor pattern findings with the Mahalanobis·Taguchi Adjoint method].

There are many autoimmune liver diseases in which diagnosis is difficult so that overlap is accepted, and this negatively affects treatment. The initial diagnosis is therefore important for later treatment and convalescence. We distinguished autoimmune cholangitis, autoimmune hepatitis and primary biliary cirrhosis by the Mahalanobis·Taguchi Adjoint (MTA) method in the Mahalanobis·Taguchi system and analyzed the pattern of factor effects by the MTA method. As a result, the characteristic factor effect pattern of each disease was classified, enabling the qualitative evaluation of cases including overlapping cases which were difficult to diagnose.