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.

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.

Less Social Support for Patients With COVID-19: Comparison With the Experience of Nurses.

Context: Since December 2019, more than 80,000 patients have been diagnosed with coronavirus disease 2019 (COVID-19) in China. Social support status of COVID-19 patients, especially the impact of social support on their psychological status and quality of life, needs to be addressed with increasing concern. Objectives: In this study, we used social support rating scale (SSRS) to investigate the social support in COVID-19 patients and nurses. Methods: The present study included 186 COVID-19 patients at a Wuhan mobile cabin hospital and 234 nurses at a Wuhan COVID-19 control center. Responses to a mobile phone app-based questionnaire about social support, anxiety, depression, and quality of life were recorded and evaluated. Results: COVID-19 patients scored significantly lower than nurses did on the Social Support Rating Scale (SSRS). Among these patients, 33.9% had anxiety symptoms, while 23.7% had depression symptoms. Overall SSRS, subjective social support scores and objective support scores of patients with anxiety were lower than those of patients without anxiety. This result was also found in depression. In addition, all dimensions of social support were positively correlated with quality of life. Interestingly, in all dimensions of social support, subjective support was found to be an independent predictive factor for anxiety, depression, and quality of life, whereas objective support was a predictive factor for quality of life, but not for anxiety and depression via regression analysis. Conclusion: Medical staffs should pay attention to the subjective feelings of patients and make COVID-19 patients feel respected, supported, and understood from the perspective of subjective support, which may greatly benefit patients, alleviate their anxiety and depression, and improve their quality of life.

Addressing the Licensed Doctor Maldistribution in China: A Demand-And-Supply Perspective.

Background: The maldistribution of licensed doctors is one of the major challenges faced by the Chinese health sector. However, this subject remains underexplored, as the underlying causes of licensed doctor distribution have not been fully mapped out. To fill the research void, this study theoretically modeled and empirically measured various determinants of licensed doctor distribution from both the supply and demand sides while taking the spillover effect between the adjacent geographical units into consideration. Methods: The theory of demand and supply is adopted to construct a research framework so as to explain the imbalance in the licensed doctor distribution. Both direct effects and spillover effects of the supply-side factors and demand-side factors are empirically measured with the spatial panel econometric models. Results: The health service demand was found, as expected, to be the major driving force of the licensed doctor distribution across the nation. That is, the increase in health services demands in a province could significantly help one unit attract licensed doctors from adjacent units. Unexpectedly but intriguingly, the medical education capacity showed a relatively limited effect on increasing the licensed doctor density in local units compared with its spillover effect on neighboring units. In addition, government and social health expenditures played different roles in the health labor market, the former being more effective in increasing the stock of clinicians and public health doctors, the latter doing better in attracting dentists and general practitioners. Conclusions: The results provide directions for Chinese policy makers to formulate more effective policies, including a series of measures to boost the licensed doctor stock in disadvantaged areas, such as the increase of government or social health expenditures, more quotas for medical universities, and the prevention of a brain drain of licensed doctors.