Analyzing the efficiency of Chinese primary healthcare institutions using the Malmquist-DEA approach: Evidence from urban and rural areas

Abstract

Background: China has been increasing the investment in Primary Health Care Institutions (PHCIs) since the launch of the New Health Care System Reform in 2009. It is a crucial concern whether the PHCIs can meet residents' need both in urban and rural with the limited government finance, especially encountering the challenge of the COVID-19. This study aimed to reveal the trend of the primary health service efficiency in the past decade, compare the urban-rural differences, and explore relevant factors.

Methods: DEA and Malmquist models were applied to calculate the health service efficiency of PHCIs among 28 provinces in China, with the input variables including the number of institutions, number of beds, number of health technicians, and the outputs variables including the number of outpatients and emergency visits, number of discharged patients. And the Tobit model was used to analyze the factors on the efficiency in urban and rural. A sensitivity analysis for model validations was also carried out.

Results: The average technical efficiency (TE) of urban PHCIs fluctuated from 63.3% to 67.1%, which was lower than that in rural (75.8-82.2%) from 2009 to 2019. In terms of dynamic efficiency, the urban PHCIs performed better than the rural, and the trends in the total factor productivity change were associated with favorable technology advancement. The population density and dependency ratio were the key factors on TE in both of the urban and rural PHCIs, and these two factors were positively correlated to TE. In terms of TE, it was negatively correlated with the proportion of total health expenditure as a percentage of GDP in urban PHCIs, while in rural it was positively correlated with the urbanization rate and negatively correlated with GDP per capita. Besides, the tests of Mann-Whitney U, and Kruskal-Wallis H indicated the internal validity and robustness of the chosen DEA and Malmquist models.

Conclusions: It needs to reduce the health resource wastes and increase service provision in urban PHCIs. Meanwhile, it is necessary to strengthen medical technology and gaining greater efficiency in rural PHCIs by technology renovation.

Keywords: China; Malmquist index; data envelopment analysis; primary healthcare institutions; relative efficiency; urban-rural areas.