基于数据包络分析的北京市精神卫生资源配置效率研究

目的 评价十三五期间北京市精神卫生资源配置效率变化，为优化北京市精神卫生资源配置提供依据。方法 采用DEA方法中的BCC模型和Malmquist指数模型对北京市精神卫生资源分别进行静态和动态分析。结果 2019年北京市仅有5个行政区实现DEA有效，综合效率均值为0.673，纯技术效率为0.882，规模效率为0.760；生态涵养区综合效率最高，平原山区新城次之、中心城区与城市副中心功能区最低;2015—2019年间北京市精神卫生资源配置全要素生产率均值为1.080，技术进步率是卫生资源配置的效率提升主要动力。结论 总体上看，十三五期间北京市精神卫生资源配置效率有所提高，整体水平仍有待提升，资源配置综合效率地区空间分布差异明显，资源冗余与资源匮乏共存，各功能区资源配置全要素生产率存在差异，平原山区新城效率最低。建议加大财政投入与支付转移，缩小区间差异，合理调配资源，避免盲目投入，据各功能区定位，全面提高精神卫生资源配置效率。

A study on the efficiency of mental health resource allocation in Beijing based on data envelopment analysis

Objective To evaluate the changes of mental health resource allocation efficiency in Beijing during the 13th Five-Year Plan period and provide a basis for optimizing mental health resource allocation in Beijing. Methods The BCC model and Malmquist index model in data envelopment analysis (DEA) method were used to analyze the mental health resources in Beijing city statically and dynamically, respectively. Results In 2019, only five administrative districts in Beijing achieved DEA effectiveness, with a mean value of 0.673 for comprehensive efficiency, 0.882 for pure technical efficiency, and 0.760 for scale efficiency; the ecological cultured area had the highest comprehensive efficiency, followed by the new city in the plain mountainous area and the lowest in the central city and the sub-city functional area; the mean value of mental health in Beijing between 2015 and 2019. The average value of total factor productivity of resource allocation was 1.080, and the rate of technological progress is the main driver of efficiency improvement of health resource allocation. Conclusion Overall, the efficiency of mental health resource allocation in Beijing during the 13th Five-Year Plan period has improved, and the overall level still needs to be improved. The regional spatial distribution of the comprehensive efficiency of resource allocation varies significantly, and resource redundancy and resource scarcity coexist. There are differences in the total factor productivity of resource allocation in each functional area, with the lowest efficiency in the new cities in the plains and mountains. It is recommended to increase financial input and payment transfer, narrow the differences between districts, reasonably allocate resources, avoid blind input, and improve the overall mental health resource allocation efficiency according to the positioning of each functional area.