AUTHOR=Liang Binghua , Huang Lifeng , Chen Zhuo , Hao Bangyan , Li Chengcheng TITLE=Regional differences, dynamic evolution, and influencing factors of high-quality medical resources in China’s ethnic minority areas JOURNAL=Frontiers in Public Health VOLUME=12 YEAR=2024 URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1436244 DOI=10.3389/fpubh.2024.1436244 ISSN=2296-2565 ABSTRACT=Background

In China, as people’s standard of living improves and the medical service system becomes more sophisticated, the demand for higher-quality and improved healthcare services is steadily rising. Inequality in health resource allocation (HRA) is more pronounced in ethnic minority areas (EMAs) than in developed regions. However, little research exists on high-quality medical resources (HQMRs) in China’s EMAs. Hence, we examined the spatiotemporal dynamic evolution of HQMRs in China’s EMAs from 2007 to 2021 and identified the main factors affecting their respective HQMR levels.

Methods

We selected tertiary hospitals to represent the quality of healthcare resources. We employed descriptive statistical techniques to analyze changes in the distribution of HQMRs from 2007 to 2021. We used the Dagum Gini coefficient and kernel density approach to analyze the dynamic evolution of HQMRs in China’s EMAs. We utilized the least squares dummy variable coefficient (LSDVC) to identify key factors affecting HQMR.

Results

The number of HQMRs in each EMA has risen annually. The average number of tertiary hospitals increased from 175 in 2007 to 488 in 2021. The results of the Dagum Gini coefficient revealed that the differences in the HQMR level in China’s EMAs have slowly declined, and intra-regional disparities have now become the primary determining factor influencing overall variations. The kernel density plot indicated that the HQMR level improved significantly during the study period, but bifurcation became increasingly severe. Using the LSDVC for analysis, we found that gross domestic product (GDP) per capita, the size of the resident population, and the number of students enrolled in general higher education exhibited a significant negative correlation with HQMR levels, while GDP and urbanization rate had a significant promoting effect.

Conclusion

The HQMR level in EMAs has risen rapidly but remains inadequate. The differences in HQMR between regions have continued to narrow, but serious bifurcation has occurred. Policymakers should consider economic growth, education, and population size rather than simply increasing the number of HQMRs everywhere.