Kong Dechen, Jiang Nan, He Xiaomin, Yuan Jing, Du Qing, Lian Wu
Health Management School of Binzhou Medical University, Yantai, Shandong, People's Republic of China.
Risk Manag Healthc Policy. 2025 Mar 13;18:869-889. doi: 10.2147/RMHP.S500994. eCollection 2025.
Enhancing health productivity is a pressing priority to promote the Healthy China Initiative. This study aims to assess the efficiency of health production and to analyze the disparities in efficiency across regions.
A multi-dimensional approach is used to assess the health efficiency of 31 provinces in China over the period 2010 to 2020. The analysis incorporates the conventional BCC model, the super-efficient SBM model, and the Malmquist index model within the framework of DEA modeling. And using the Dagum Gini coefficient to further analyze the differences in health productivity of China.
The BCC model calculated China's comprehensive health production efficiency in 2020 to be 0.732. The SBM model assessed the average health productivity value among China's provinces in 2020, revealing Guangdong as the highest (2.276) and Qinghai as the lowest (0.351). The average value of China's Malmquist Index from 2010 to 2020 was 1.002, indicating a slight overall improvement in health production efficiency. Furthermore, the score of technological change and technological efficiency change in five provinces were more than 1. Gini coefficient had obvious downward trend from 2010 to 2020, and there was a lower level in the northeastern (0.055) and eastern (0.0989) regions.
Though the whole health productivity of China has been on the rise, health production efficiency in many provinces still needs to be improved. Inequities in health services provision persist, particularly between the eastern and western regions. The government should play a significant role in establishing standardized criteria for regular evaluation of health production efficiency levels. It's suggested to utilize digital health technologies to facilitate the exchange of information among different regions in China, thereby fostering collaborative efforts to improve overall health outcomes.
提高健康生产力是推进健康中国行动的当务之急。本研究旨在评估健康生产效率，并分析各地区效率差异。
采用多维度方法评估2010年至2020年中国31个省份的健康效率。分析在数据包络分析（DEA）建模框架内纳入了传统的BCC模型、超效率SBM模型和Malmquist指数模型。并利用达古姆基尼系数进一步分析中国健康生产力的差异。
BCC模型计算得出2020年中国综合健康生产效率为0.732。SBM模型评估了2020年中国各省的平均健康生产力值，显示广东省最高（2.276），青海省最低（0.351）。2010年至2020年中国Malmquist指数的平均值为1.002，表明健康生产效率总体略有提高。此外，五个省份的技术变化和技术效率变化得分均超过1。基尼系数从2010年到2020年呈明显下降趋势，东北地区（0.055）和东部地区（0.0989）处于较低水平。
尽管中国整体健康生产力一直在上升，但许多省份的健康生产效率仍有待提高。卫生服务提供方面的不平等依然存在，尤其是东西部地区之间。政府应在建立定期评估健康生产效率水平的标准化标准方面发挥重要作用。建议利用数字健康技术促进中国不同地区之间的信息交流，从而促进共同努力改善整体健康结果。
