Measuring the Inequalities in the Distribution of Public Healthcare Resources by the HRDI (Health Resources Density Index): Data Analysis from 2010 to 2019

Interprovincial spatial distribution patterns and socioeconomic factors on traditional Chinese medicine (TCM) service utilization in China

The traditional Chinese medicine (TCM) industry in China exhibits significant regional disparities in health service utilization, the underlying reasons for which are yet to be fully explored. This study employs Geodetector models to analyze the factors affecting TCM service utilization, providing the first examination of spatial distribution patterns and influencing factors for both TCM outpatient (TCMOSU) and inpatient services (TCMISU). The findings of this study reveal spatial disparities across China's provinces, showing a prevalence of TCMOSU in the east and TCMISU decreasing from southwest to northeast. Global Moran's I autocorrelation analysis revealed a positive spatial correlation between TCMOSU and TCMISU across Chinese provinces, suggesting spatial clustering and the potential for interregional collaboration in the development of TCM services. Local Moran's I autocorrelation analysis revealed clusters of TCMOSU in wealthier eastern provinces, such as Jiangsu and Tianjin, and clusters of TCMISU in the southwest. Factor detector analysis revealed that disposable income per capita was the most significant factor linking higher incomes with increased TCMOSU. In contrast, TCMISU was primarily influenced by demographic factors, such as the illiteracy rate and population urbanization rate, emphasizing traditional practices in lower education regions. Interaction detector analysis revealed the joint effects of these factors, demonstrating how regional economic status, health status, and healthcare resource indicators interact with other factors for TCMOSU and how demographic factors significantly influence the prevalence of TCMISU. This study highlights the importance of considering health status together with regional economic, demographic, and healthcare resources when formulating TCM healthcare policies and allocating such resources in China. Promoting the balanced and coordinated regional development of TCM services across the country requires the development of strategies that account for these varied regional characteristics.

Ethnic disparities and temporal trends in health resource allocation: a retrospective decadal analysis in Sichuan, a multi-ethnic Province of Southwest China (2009-2019)

BACKGROUND

Research on health resource allocation trends in ethnic minority and impoverished areas in China is limited since the 2009 Medical Reform. This study aimed to investigate the variations and inequalities in health resource distribution among ethnic minority, poverty-stricken, and non-minority regions in Sichuan Province, a multi-ethnic province in Southwest China, from 2009 to 2019.

METHODS

The numbers of beds, doctors and nurses were retrospectively sourced from the Sichuan Health Statistics Yearbook between 2009 and 2019. All the 181 counties in Sichuan Province were categorized into five groups: Yi, Zang, other ethnic minority, poverty-stricken, and non-minority county. The Theil index, adjusted for population size, was used to evaluate health resource allocation inequalities.

RESULTS

From 2009 to 2019, the number of beds (Bedp1000), doctors (Docp1000), and nurses (Nurp1000) per 1000 individuals in ethnic minority and poverty-stricken counties consistently remained lower than non-minority counties. The growth rates of Bedp1000 in Yi (140%) and other ethnic minority counties (127%) were higher than in non-minority counties (121%), while the growth rates of Docp1000 in Yi (20%) and Zang (11%) counties were lower than non-minority counties (61%). Docp1000 in 33% and 50% of Yi and Zang ethnic counties decreased, respectively. Nurp1000 in Yi (240%) and other ethnic minority (316%) counties increased faster than non-minority counties (198%). The Theil index for beds and nurses declined, while the index for doctors increased. Key factors driving increases in bed allocation include preferential policies and economic development levels, while health practitioner income, economic development levels and geographical environment significantly influence doctor and nurse allocation.

CONCLUSIONS

Preferential policies have been successful in increasing the number of beds in health facilities, but not healthcare workers, in ethnic minority regions. The ethnic disparities in doctor allocation increased in Sichuan Province. To increase the number of doctors and nurses in ethnic minority and poverty-stricken regions, particularly in Yi counties, more preferential policies and resources should be introduced.

Human Resources Allocation of the Centers for Disease Control and Prevention in China

Objective

To analyze the equity of human resources allocation of the Centers for Disease Control and Prevention (CDCs) and to predict the development in the next five years in China, and to provide a scientific basis for promoting the development of human resources.

Methods

The data of the CDCs from 2017 to 2021 were obtained from the "China Health Statistical Yearbook", and descriptive analysis, health resource density index (HRDI), Theil index, and health resource agglomeration degree (HRAD) were used to evaluate the equity, and the grey prediction model GM (1, 1) was used to predict the development from 2022 to 2026.

Results

Measured by the HRDI, the shortage of human resources in the western region was relatively obvious, with a shortage of more than 11,656 health technicians, more than 6418 licensed (assistant) physicians, and more than 693 registered nurses. The Theil index of human resources allocation by population was between 0.016 and 0.071, and the Theil index of human resources allocation by geography was between 0.312 and 0.359. The allocation of human resources by geography was more unequal than those allocated by population. In terms of HRAD, human resources are over-allocated equitably by geography in the eastern and central regions, while they are under-allocated equitably by geography in the western region. In terms of the difference between the HRAD and PAD, the eastern region has a shortage of human resources relative to the concentration population, and the western region has an excess of human resources relative to the concentration population.

Conclusion

The human resources allocation of the CDCs in China was uneven. The human resources of the CDCs were allocated more equitably by population than by geography. There was a situation where the equity of human resource allocation of the CDCs was contrary to the actual demand for medical care.

Revealing spatiotemporal inequalities, hotspots, and determinants in healthcare resource distribution: insights from hospital beds panel data in 2308 Chinese counties

BACKGROUND

Ensuring universal health coverage and equitable access to health services requires a comprehensive understanding of spatiotemporal heterogeneity in healthcare resources, especially in small areas. The absence of a structured spatiotemporal evaluation framework in existing studies inspired us to propose a conceptual framework encompassing three perspectives: spatiotemporal inequalities, hotspots, and determinants.

METHODS

To demonstrate our three-perspective conceptual framework, we employed three state-of-the-art methods and analyzed 10 years' worth of Chinese county-level hospital bed data. First, we depicted spatial inequalities of hospital beds within provinces and their temporal inequalities through the spatial Gini coefficient. Next, we identified different types of spatiotemporal hotspots and coldspots at the county level using the emerging hot spot analysis (Getis-Ord Gi* statistics). Finally, we explored the spatiotemporally heterogeneous impacts of socioeconomic and environmental factors on hospital beds using the Bayesian spatiotemporally varying coefficients (STVC) model and quantified factors' spatiotemporal explainable percentages with the spatiotemporal variance partitioning index (STVPI).

RESULTS

Spatial inequalities map revealed significant disparities in hospital beds, with gradual improvements observed in 21 provinces over time. Seven types of hot and cold spots among 24.78% counties highlighted the persistent presence of the regional Matthew effect in both high- and low-level hospital bed counties. Socioeconomic factors contributed 36.85% (95% credible intervals [CIs]: 31.84-42.50%) of county-level hospital beds, while environmental factors accounted for 59.12% (53.80-63.83%). Factors' space-scale variation explained 75.71% (68.94-81.55%), whereas time-scale variation contributed 20.25% (14.14-27.36%). Additionally, six factors (GDP, first industrial output, local general budget revenue, road, river, and slope) were identified as the spatiotemporal determinants, collectively explaining over 84% of the variations.

CONCLUSIONS

Three-perspective framework enables global policymakers and stakeholders to identify health services disparities at the micro-level, pinpoint regions needing targeted interventions, and create differentiated strategies aligned with their unique spatiotemporal determinants, significantly aiding in achieving sustainable healthcare development.

Estimating the Distribution and Convergence of County-level Healthcare Resources Allocative Efficiency in China Based on DEA and Spatial Panel Model

Although China's 2009 New Healthcare Reform aimed to correct the imbalance in the spatial allocation of healthcare resources with a focus on the county level, its impact on county-level allocative efficiency evolution and convergence remains unclear. This paper for the first time performs a spatial analysis to explore the distribution, evolution, and convergence of the allocative efficiency of healthcare resources with county-level data. This paper uses the sample data of 158 countries in Henan Province, China, to evaluate the evolution and convergence of the allocative efficiency of healthcare resources. Based on the estimated Data Envelopment Analysis (DEA) allocative efficiency, analysis of variance (ANOVA), and spatial descriptive analysis, we explore the county heterogeneity and efficiency evolution; a spatial panel model is then utilized to test the county-level convergence of the allocative efficiency of healthcare resources. Although the number of efficient counties has not increased, the number of inefficient individuals keeps decreasing, and the allocative efficiency of municipal districts is lower than that of nonmunicipal counties. There exists a positive spatial correlation of allocative efficiency in Henan Province, and significant and robust convergence results can be found at the county level after China's 2009 reform. This study reveals a diversified picture of China's county-level spatial evolution of allocative efficiency in healthcare resources, showing a more balanced spatial distribution of allocative efficiency since the triggering of China's 2009 reform. However, long-term investment incentives and a targeted allocation of healthcare resources are still needed to promote further efficiency convergence and increase the number of counties with efficiency.