Detecting the priority areas for health workforce allocation with LISA functions: an empirical analysis for China.

BACKGROUND: Health workforce misdistribution leads to severe inequity and low-efficiency in health services in the developing countries. Targeting at China, this research aims to reveal, visualize and compare the geographical distribution patterns of different subtypes of urban and rural health workforce and identify the priority regions for health workforce planning and allocation policies designing. METHODS: The health workforce density (workforce-to-population ratio) is adopted to represent the accessibility to health workforce in each geographical unit. Besides a descriptive geography of health workforce as a whole, the local indicators of spatial association (LISA) are used to explore the spatial clusters of different subtypes of health workforce, which are visualized by geographical tools. RESULTS: Results reveal that regional disparities and spatial clusters exist in China's health workforce distribution, with different types of workforce exhibiting relatively different spatial distribution characteristics. Besides, huge urban-rural disparities are found in the distribution of health workforce in China. Unexpectedly but intriguingly, most of the high-high and high-low cluster area of urban health workforce are concentrated in the western China (Xinjiang, Xizang etc.), indicating the relative abundant stock of urban health workforce in these units, while the low-low and low-high cluster area of different types of urban health workforce are mainly distributed in middle China. Regarding the rural health workforce, there is an obvious and similar low-low and low-high clustering pattern in western provinces (Sichuan, Yunnan) for the licensed doctors, pharmacists, technologists, which play a critical role in health services delivery. CONCLUSIONS: Different types of health workforce displayed distinct spatial distribution patterns, while the misdistribution of rural health workforce imposed more challenges to the Chinese health sector due to its poorer stock and more disadvantaged positions of backward regions (i.e., low-low and low-high cluster area). Subtype-specific and region-oriented health workforce planning and allocation policies are suggested to be made, aiming at the urban and rural health workforce respectively, by prioritizing the identified low-low and low-high cluster areas.

The Association between Health Workforce and Health Outcomes: A Cross-Country Econometric Study.

This study investigates the strength and significance of the associations of health workforce with multiple health outcomes and COVID-19 excess deaths across countries, using the latest WHO dataset. Multiple log-linear regression analyses, counterfactual scenarios analyses, and Pearson correlation analyses were performed. The average density of health workforce and the average levels of health outcomes were strongly associated with country income level. A higher density of the health workforce, especially the aggregate density of skilled health workers and density of nursing and midwifery personnel, was significantly associated with better levels of several health outcomes, including maternal mortality ratio, under-five mortality rate, infant mortality rate, and neonatal mortality rate, and was significantly correlated with a lower level of COVID-19 excess deaths per 100 K people, though not robust to weighting by population. The low density of the health workforce, especially in relatively low-income countries, can be a major barrier to improving these health outcomes and achieving health-related SDGs; however, improving the density of the health workforce alone is far from enough to achieve these goals. Our study suggests that investment in health workforce should be an integral part of strategies to achieve health-related SDGs, and achieving non-health SDGs related to poverty alleviation and expansion of female education are complementary to achieving both sets of goals, especially for those low- and middle-income countries. In light of the strains on the health workforce during the current COVID-19 pandemic, more attention should be paid to health workforce to strengthen health system resilience and long-term improvement in health outcomes. Supplementary Information: The online version contains supplementary material available at 10.1007/s11205-022-02910-z.

Spatial Disparities in Access to Healthcare Professionals in Sichuan: Evidence from County-Level Data.

As a southwestern province of China, Sichuan is confronted with geographical disparities in access to healthcare professionals because of its complex terrain, uneven population distribution and huge economic gaps between regions. With 10-year data, this study aims to explore the county-level spatial disparities in access to different types of healthcare professionals (licensed doctors, registered nurses, pharmacists, technologists and interns) in Sichuan using temporal and spatial analysis methods. The time-series results showed that the quantity of all types of healthcare professionals increased, especially the registered nurses, while huge spatial disparities exist in the distribution of healthcare professionals in Sichuan. The local Moran's I calculations showed that high-high clusters (significantly high healthcare professional quantity in a group of counties) were detected in Chengdu (capital of Sichuan) and relatively rich areas, while low-low clusters (significantly low healthcare professional quantity in a group of counties) were usually found near the mountain areas, namely, Tsinling Mountains and Hengduan Mountains. The findings may deserve considerations in making region-oriented policies in educating and attracting more healthcare professionals to the disadvantaged areas.