| 引用本文：于菲, 耿顺利, 高建民, 范小静, 董琬月, 吕强.基于ARIMA和GM(1,1)模型的陕西省个人卫生支出预测[J].中国卫生政策研究,2018,11(7):19-23 |
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| 基于ARIMA和GM(1,1)模型的陕西省个人卫生支出预测 |
| 投稿时间：2017-07-23 修订日期：2018-03-24 PDF全文浏览 HTML全文浏览 |
| 于菲1, 耿顺利1, 高建民2, 范小静1, 董琬月1, 吕强1 |
1. 西安交通大学医学院 陕西西安 710061;
2. 西安交通大学公共政策与管理学院 陕西西安 710049 |
| 摘要:目的：评价陕西省疾病经济负担的变化趋势，为卫生管理者进行政策调整和干预提供理论依据。方法：采用ARIMA和GM （1，1）模型对2016—2020年陕西省个人现金卫生支出和其占卫生总费用比例进行测算。结果：2016—2020年间，陕西省OOP占卫生总费用比重将由32.69%降至26.92%。结论：陕西省居民OOP占卫生总费用比重在2016—2020年不断下降，表明陕西省居民个人卫生支出负担正逐渐减轻，卫生筹资结构逐步趋于合理。建议进一步加大政府和社会卫生投入，合理提高社会医疗保险报销比例，充分发挥社会医疗保险的作用。 |
| 关键词:个人卫生支出 疾病经济负担 时间序列预测 |
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| Prediction on the personal out-of-pocket healthcare expenses in Shaanxi Province based on ARIMA and GM (1, 1) model |
| YU Fei1, GENG Shun-li1, GAO Jian-min2, FAN Xiao-jing1, DONG Wan-yue1, LV Qiang1 |
1. Xi'an JiaoTong University Health Science Center, Xi'an Shanxi 710061, China;
2. School of Public Policy and Administration, Xi'an JiaoTong University, Xi'an Shanxi 710049, China |
| Abstract:Objective:This paper evaluates the changing trend of medical economic burden in Shaanxi province and provides theoretical basis and policy suggestions for policy adjustment and intervention of health managers. Methods:To forecast and evaluate OOP and account for the proportion of total health expenditure in 2016-2020 in Shaanxi Province through the methods of ARIMA, GM (1,1). Results:The forecast shows that from 2016 to 2020, the proportion of OOP in NHE will reduce from 32.69% to 26.92%.Conclusions:Shaanxi Province residents' OOP accounting for the proportion of total health expenditure in 2016-2020 will continue to decline. It shows that the burden of personal health expenditure is gradually alleviating in Shaanxi residents, and the health financing structure is becoming more and more reasonable. We must further increase government and social health input, reasonably improve the proportion of reimbursement of social medical insurance, and give full play to the supplementary role of social medical insurance. |
| Key words:Out-of-Pocket payments Economic burden of disease Time series prediction method |
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