Objective To Provide statistical references for disease-based payment reform with Diagnosis Related Groups (DRGs). Methods Based on 1 969 stroke inpatients from two hospitals in Chongqing city, we used classification and regression trees (CART) of decision tree to establish classification regulations of the case-mix model for stroke inpatients, and multivariate statistical model to evaluate whether the case-mix could provide a satisfactory prediction to costs for stroke inpatients in comparison with the foreign model. Results ① The classification nodes of our model were surgical procedure, nursing care degree, and hospital infection respectively by which 1 969 stroke inpatients were divided into 5 groups. The classification nodes in foreign model were surgical procedure, age≥50 years, and whether patients would refer to other institutions after leaving the hospitals by which 1 969 stroke inpatients were also classified into 5 groups. ② For medical institutions and the third payers, we found that the data from our model could explain 80.46% of the total costs and 16.58% for individual inpatient, which were higher than that of foreign model (76.87% for medical institutions and the third payers, 9.13% for individuals ). Conclusions Compared with foreign model, our model is more suitable for the situation in China. The study is only based on 1 969 stroke inpatients from south west part of China, so the conclusion needs further studies to confirm.
Objective To provide references to control the cost of stroke inpatients by analysing pertinent factors of stroke inpatients. Methods According to the models of Anderson and Newnan, univariable analysis and multivariable statistical analysis were applied to a number of factors including predisposing factors, enabling factors, and needs factors in 1 969 stroke inpatients of two third level first-class hospitals in Chongqing. Results Among the 1 969 stroke inpatients, 64% had a history of hypertension, and 50% exhibited hypertension during their stay in hospital. Expenditure on medication consumed the highest costs (51%). Length of stay was the most important factor affecting inpatient expense, additional factors were number of surgical operation, nurse type, Rankin score, number of complications etc. Conclusions Complex measures focusing on hypertension to prevent and control of stroke are recommended. Reducing unnecessary stay in hospital and appropriate prescribing are important methods to reduce cost of stroke inpatients.