| 摘要: |
| 目的 研究2011-2016年上海市某三甲医院慢性肾脏病（CKD）以及患病人数最多的前3类疾病（慢性肾功能衰竭、原发性肾病综合征、原发性肾小球肾炎）住院患者的床日住院费用、药占比的变化趋势，并预测2017年的床日住院费用和药占比。方法 分析CKD住院患者床日住院费用和药占比的总体情况。应用SPSS 23.0软件时间序列分析模块中的专家建模器进行建模研究。首先利用2011年1月-2015年12月CKD住院患者的床日住院费用、药占比数据建模，用所建模型预测的2016年床日住院费用和药占比与2016年的真实数据进行对比，并评估模型效果；然后用模型预测2017年CKD住院患者的床日住院费用和药占比；最后，依照相同原理，预测2017年慢性肾功能衰竭、原发性肾病综合征、原发性肾小球肾炎住院患者的床日住院费用和药占比情况。结果 2011-2016年该院CKD住院患者床日住院费用、人均住院总费用、人均药品费用逐年上升，2016年分别达到（1 506.89±1 003.34）元、8 252.51元、2 009.88元，增长幅度分别为62.38%、41.62%、14.44%；药占比逐年下降，2016年达到32.33%。CKD住院患者床日住院费用、药占比所构建的时间序列模型分别为温特斯加性模型和简单季节性模型。依照时间序列模型预测的2016年的床日住院费用和药占比与真实数据基本吻合。预测2017年CKD住院患者的床日住院费用为1 600元左右、药占比为30%左右；单独预测的2017年前3类疾病住院患者床日住院费用分别为1 700元、1 800元、1 600元左右，药占比分别为30%、35%、10%左右。结论 该医院收入结构不断优化；建议建立实时疾病费用监控信息平台；基于单病种费用变化趋势研究有待进一步深入。 |
| 关键词: 慢性肾脏病 住院天数 费用 药品收入 时间序列分析 建模器 上海 |
| DOI：10.16781/j.0258-879x.2018.01.0029 |
| 投稿时间：2017-12-08修订日期：2018-01-01 |
| 基金项目:上海市公共卫生体系建设三年行动计划（SCREENING STUDY GWIV-18）. |
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| Time series analysis of per diem expense of inpatients with chronic kidney disease in a tertiary referral hospital in Shanghai |
| LONG Jun-rui1,SHAN Chan-juan1,WANG Jiu-sheng2,MEI Chang-lin2,XIONG Lin-ping1* |
(1. Department of Health Service Management, Faculty of Health Service, Second Military Medical University, Shanghai 200433, China;
2. Department of Nephrology, Kidney Institute of PLA, Changzheng Hospital, Second Military Medical University, Shanghai 200003, China
*Corresponding author) |
| Abstract: |
| Objective To study the tendency of per diem expense and drug cost ratio of inpatients with chronic kidney disease (CKD) and the top three diseases (chronic renal failure, nephrotic syndrome, and primary glomerulonephritis) in a tertiary referral hospital in Shanghai from 2011 to 2016, so as to predict the per diem expense and drug cost ratio of inpatients in 2017. Methods We analyzed the per diem expense, drug cost ratio and other general situation of inpatients with CKD, and conducted the modeling research by the expert modeler in SPSS 23.0 software time series analysis module. Firstly, the per diem expense and drug cost ratio of inpatients with CKD from Jan. 2011 to Dec. 2015 were used to build model, and then the per diem expense and drug cost ratio of the inpatients in 2016 predicted by the model were compared with the real data of the inpatients in 2016 and the values of the model were evaluated. Secondly, the per diem expense and drug cost ratio of inpatients with CKD in 2017 were predicted. Finally, according to the same principle, the per diem expense and drug cost ratio of inpatients with chronic renal failure, nephrotic syndrome and primary glomerulonephritis in 2017 were predicted. Results The per diem expense, per capita hospitalization expense and per capita drug expense of inpatients with CKD increased annually from 2011 to 2016, and the expenses were (1 506.89±1 003.34) yuan, 8 252.51 yuan and 2 009.88 yuan in 2016, respectively, with the growth rates being 62.38%, 41.62% and 14.44%, respectively. The drug cost ratio of the inpatients decreased annually, and the ratio reached 32.33% in 2016. The time series models established by the per diem expense and drug cost ratio of inpatients with CKD were Winters additive model and simple seasonal model, respectively. According to the time series model, the predicted per diem expense and drug cost ratio in 2016 were consistent with the real values in 2016. The per diem expense and drug cost ratio of inpatients with CKD in 2017 predicted by the time series model were about 1 600 yuan and 30%, respectively. The predicted per diem expenses of inpatients with chronic renal failure, nephrotic syndrome and primary glomerulonephritis in 2017 were about 1 700 yuan, 1 800 yuan and 1 600 yuan, respectively, and the predicted drug cost ratios were about 30%, 35% and 10%, respectively. Conclusion The hospital income structure is constantly optimized, and a real-time disease cost monitoring information platform should be established. The research based on the trend of single disease expense needs to be further studied. |
| Key words: chronic kidney disease hospital stay expense medicine income time series analysis model builder Shanghai |
