Analysis of medical resources for allocation equity using traditional Chinese medicine resource as a model. (19th August 2022)

• Record Type: Journal Article
• Authors: Li, Junshan; Wu, Yilin; Lu, Yuanan
• Is Part Of: International journal of health planning and management. Volume 37:Number 6(2022)
• Page Start: 3205
• Page End: 3217
• Publication Date: 2022-08-19
• Subjects: allocation fairness -- China -- Gini coefficient -- Lorentz curve -- TCM resources; Health planning -- Periodicals; Health services administration -- Periodicals; Santé publique -- Planification -- Périodiques; Santé, Services de -- Administration -- Périodiques; 362.1068
• DOI: 10.1002/hpm.3549
• Languages: English
• ISSNs: 0749-6753

Abstract

Abstract: Objectives: This study is designed to analyse current allocation equity of medical resources in China for a better distribution of medical resources. Methods: Descriptive statistical methods were used to analyse the overall allocation of Traditional Chinese medicine (TCM) resources between 2012 and 2018. Lorentz curve and Gini coefficient were used to quantitatively analyse the fairness of the allocation from the population and geography two dimensions. Results: This study revealed an increase of TCM resources for the 6‐year period, but the fair allocation of these resources was subjected to the methods used. The Gini coefficients were <0.3 based on population distribution but >0.5 basing on the geography allocation. Conclusion: Population based analysis for the equity of the TCM resource allocation is superior, more attention for health resource planning is needed to focus on geographical fairness in the future, especially for the less populated rural regions. Highlights: Analysis of fairness allocation of medical resources using Traditional Chinese medicine (TCM) resource as a model. Quantitative study of allocation fairness of TCM resources with Lorentz curve and Gini index. The equity of TCM resource distribution is better presented by a population than a geography. Demonstrated the need for better planning of health resources for less populated rural regions.