Study of Influential Factors of Provincial Health Expenditure -Analysis of Panel Data After the 2009 Healthcare Reform in China

Hou, Ji-Fei; Zhang, Yun; Liu, Yanzheng; Li, Jing; Wang, Yue

doi:10.21203/rs.3.rs-15700/v1

Cited by 1 publication

(2 citation statements)

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“…The gray parameter, , represents the vector composed of the system development parameter, and the driving parameters can be obtained according to the least-squares method according to Eq. (12).…”

Section: Traditional Gray Modelmentioning

confidence: 99%

“…Therefore, scholars have often selected driving factors according to the characteristics or hot issues of the study area. Previous studies [8,12] have employed instrumental variable quantile regression or generalized estimating equation methods for panel models to analyze THE, and other scholars [13,14] have used logistic regression, boosted decision trees, neural networks, and the ARIMA model to predict THE. However, a common point is that the amount of data used is large and the calculations are complicated, providing no benefits for short-term analyses or situations where there is "poor information".…”

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confidence: 99%

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Total Health Expenditure and Its Driving Factors in China: A Gray Theory Analysis

Jia

Jiang

et al. 2021

Preprint

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Background: The continuous growth in total health expenditure (THE) has become a social issue of common concern in most countries. In China, THE is maintaining a rapid growth trend that is faster than that of the economy, and this trend has become increasingly obvious in the 21st century and has placed a heavy burden on the government and residents. Therefore, the aims of this paper are to analyze the main driving factors and establish a predictive model of the growth of THE in China in the 21st century.Methods: Gray system theory was employed to explore the correlation degree between THE and 9 hot topics in the areas of the economy, population, health service utilization, and policy using national data for China from 2000 to 2018. Additionally, a New Structure of the Multivariate Gray (NSGM) prediction model of health expenditure was established and compared with the traditional grey model and widely used Back Propagation (BP) neural network.Results: General government expenditures on health, the economy, and out-of-pocket health expenditures were highly correlated with THE, with all correlation degrees greater than 0.8. The correlation degrees between health institutions, population and THE were 0.6-0.8, whereas infant mortality rate and THE was only 0.573. The average of the residual percentage of the training data of the NSGM(1,10) model is 0.36%, and that of the test data is 1.85%, which is better than the results of the other models.Conclusion: The Chinese government and society have played a crucial role in reducing residents’ medical burden, whereas the improved economy and aging population have increased the demand for health services, leading to the continual increase in THE. The improved NSGM(1,N) model achieved good prediction accuracy and has unique advantages in simulating and predicting THE, which can provide a basis for policy formulation.

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Section: Traditional Gray Modelmentioning

confidence: 99%

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confidence: 99%