老龄化背景下江西省卫生总费用预测及影响因素研究

Research on the Prediction of Total Health Costs and Influencing Factors in Jiangxi Province under the Background of Aging

摘要: 描述江西省2010~2019年卫生总费用筹资结构和筹资水平，在老龄化逐步加深的情况下分析人口因素、经济因素、财政政策因素和卫生资源因素对总卫生费用的影响程度，并对卫生总费用及其构成的三大卫生费用建立GM(1,1)模型进行预测，探究其在2020~2025年的发展趋势，旨在为控制卫生费用的合理增长及优化配置提供理论依据。方法：运用灰色关联分析研究江西省卫生总费用的影响因素，采用等维递补灰色GM(1,1)模型预测2020~2025年的卫生费用。结果：农村居民人均可支配收入(0.7848)、人均GDP (0.7637)和城镇居民人均可支配收入(0.7542)代表的经济因素是影响卫生总费用的主要因素，城镇人口所占比重(0.5873)的关联度最低，而每千人口医疗机构床位数(0.6081)和每千人口卫生技术人员数(0.6345)代表的卫生资源因素处于中间水平，与卫生总费用关联度较强。另外，未来江西省卫生总费用将呈稳步增长的态势，政府卫生支出和个人卫生支出占比依旧趋近2010~2019年的波动性特征，费用支出绝对值稳定增长，社会卫生支出费用将快速增长。结论：经济因素、财政和卫生资源是江西省卫生费用增长的主要驱动因素，随着新农合逐步普及，城镇化水平对于卫生总费用的影响将减弱，但城乡卫生资源配置不公依旧明显，老龄化对卫生总费用具备重要的潜在影响。

Abstract: This paper describes the financing structure and level of total health expenses in Jiangxi Province from 2010 to 2019, analyzes the impact of population factors, economic factors, fiscal policy factors, and health resource factors on total health expenses in the context of deepening aging, and establishes a GM (1,1) model to predict the total health expenses and the three major health expenses that constitute them, to explore their development trend from 2020 to 2025. The purpose is to provide theoretical basis for controlling the reasonable growth and optimal allocation of health costs. Methods: Grey correlation analysis was used to study the influencing factors of total health expenditure in Jiangxi Province, and the isodimensional recursive grey GM (1,1) model was used to predict the health expenditure from 2020 to 2025. Results: The economic factors represented by per capita disposable income of rural residents (0.7848), per capita GDP (0.7637), and per capita disposable income of urban residents (0.7542) are the main factors that affect the total cost of health. The correlation between the proportion of urban population (0.5873) is the lowest, while the health resource factors represented by the number of beds in medical institutions per 1000 population (0.6081) and the number of health technicians per 1000 population (0.6345) are at the middle level. There is a strong correlation with total health costs. In addition, in the future, the total health expenditure in Jiangxi Province will show a steady growth trend, with the proportion of government health expenditure and personal health expenditure still approaching the volatility characteristics of 2010~2019. The absolute value of expenditure will steadily increase, and the cost of social health expenditure will grow rapidly. Conclusion: Economic factors, finance, and health resources are the main driving factors for the growth of health expenses in Jiangxi Province. With the gradual popularization of the new rural cooperative medical system, the impact of urbanization on the total health expenses will be weakened, but the inequity in the allocation of urban and rural health resources remains significant. Aging has important potential impacts on the total health expenses.