China ; Family Characteristics ; Financing, Government ; organization & administration ; Health Care Coalitions ; economics ; statistics & numerical data ; trends ; Humans ; Insurance, Health, Reimbursement ; statistics & numerical data ; trends ; Monte Carlo Method ; Rural Health

OBJECTIVETo explore the application of Monte Carlo simulation in optimizing and adjusting the reimbursement scheme with regard to the New Rural Cooperative Medical System (NCMS) to scientific steering practice. Optimization of the reimbursement scheme in rural areas of China was also studied.

METHODSA multi-stage sampling household survey was conducted in Sihui county, with 4 433 rural residents from 1 179 households from 13 towns in Guangdong province surveyed by self-designed questionnaire. Probit Regression Model was applied in fitting data and then estimating the own-price elasticity and cross elasticity of healthcare demand for both outpatients and inpatients. Monte Carlo simulation model was constructed to estimate the reimbursement effects of various alternative reimbursement schemes, by replicated simulation for one thousand times and each sampling on five hundred households. In this way, optimization of the implemented reimbursement scheme in Sihui county was conducted.

RESULTSOwn-priced elasticity of demands for outpatient visit, inpatient visit in the township hospital center, secondary hospital and tertiary hospital were -0.174, -0.264, -0.675 and -0.429, respectively. Outpatient demand was affected by the per-visit price of township hospital center and secondary hospital. The cross-priced elasticity of demands for outpatient visit appeared to be 0.125 and 0.150. The reimbursement effects of Scheme B7 showed that the efficiency of NCMS fund was 17.85% , the reimbursement ratio for healthcare was 25.63%, and the decreased percentages of poverty caused by illness was 18.25%, more than 9.37%, from the implemented scheme A. So the implemented scheme was in need for optimization.

CONCLUSIONMonte Carlo simulation technique was applicable to simulate the effects of the optimized alternative reimbursement scheme of NCMS and it provided a new idea and method to optimize and adjust the reimbursement scheme.

Adolescent ; Adult ; China ; Female ; Humans ; Insurance, Health, Reimbursement ; economics ; Male ; Middle Aged ; Monte Carlo Method ; Rural Population ; statistics & numerical data ; Young Adult

BACKGROUND: This study aimed at assessing the number of red blood cell (RBC) units transfused at different types of medical institution and examining the characteristics of transfusion recipients. METHODS: We calculated and compared the number of transfusion recipients, total RBC units transfused, and RBC units transfused per recipient. Study data were extracted from insurance benefits reimbursement claims for RBC units at the Health Insurance Review & Assessment Service from 2006 to 2010. RESULTS: Between 2006 and 2010, the number of recipients of RBC units increased from 298,049 to 376,445, the number of RBC units transfused increased from 1,460,799 to 1,841,695, and the number of RBC units transfused per recipient changed from 4.90 to 4.89. The number of recipients aged > or =65 yr increased from 133,833 (44.9%) in 2006 to 196,127 (52.1%) in 2010. The highest number of RBC units was transfused to patients with neoplastic diseases (31.9%) and diseases of the musculoskeletal system and connective tissue (14.4%). More than 80% of the total number of RBC units were transfused at tertiary and general hospitals. However, this composition rate was slightly decreasing, with the composition rate for hospitals increasing from 12.6% to 16.3%. CONCLUSIONS: This study revealed an increase in the number of RBC units transfused over a 5-yr period due to an increase in the number of transfused recipients, especially recipients aged > or =65 yr; moreover, the number of RBC units transfused differed based on medical institution type. These results provide fundamental data on RBC transfusions required for future research.
Adolescent ; Adult ; Age Factors ; Aged ; Child ; Child, Preschool ; Connective Tissue Diseases/therapy ; Databases, Factual ; Erythrocyte Transfusion/*statistics & numerical data/*trends ; Female ; Hospitals, General/statistics & numerical data ; Humans ; Infant ; Infant, Newborn ; Insurance, Health, Reimbursement/*statistics & numerical data ; Male ; Middle Aged ; Musculoskeletal Diseases/therapy ; Neoplasms/therapy ; Republic of Korea ; Sex Factors ; Tertiary Care Centers/statistics & numerical data ; Young Adult