Aim: The aim of this study was to explore the distribution and driving factors influencing the disparity of health professionals (HPs) at the Centers for Disease Control and Prevention (CDC) in China and to provide a reference for regional health planning and rational allocation of public health resources. Methods: The Gini coefficient was used to measure the equity of HP distribution at CDC sites at the provincial level during 2012–2023 in China. Moran’s I was used to analyze the spatial agglomeration effect, and the geographic detector model was used to explore the factors driving the allocation of HPs at CDC sites in different provinces. Results: The number of HPs at the CDC showed an increasing trend from 2012 to 2023 in China. The average Gini coefficients at the population and geographical areas were 0.16 and 0.58, respectively. The global Moran’s I statistic indicated a notable decline in spatial clustering for the population dimension, decreasing from 0.503 to 0.238; in contrast, spatial clustering for the geographical dimension remained relatively stable, ranging between 0.13 and 0.16. The local Moran’s I statistic revealed that provinces such as Qinghai in the western China consistently exhibited a “low–low” spatial clustering pattern. Six factors were found to explain the variability in the CDC HP distribution based on the 2020 data. In the context of factor interactions, the synergistic effects between education level and health expenditure share (q = 0.9781), and between population aging and per capita GDP (q = 0.9699), substantially exceed the explanatory power attributable to any single factor alone. Conclusions: A significant regional disparity was observed in the distribution of HPs among 31 provinces, with the distribution based on service area being less equitable than that based on population. The shortage of healthcare professionals in the western region is characterized by notably inadequate geographical distribution. Future policy initiatives should prioritize targeted spatial interventions and integrated, multi-factor collaborative strategies.

Background: Greater medical staff allocation and higher Case-Mix Index (CMI) values are both associated with increased patient medical expenditures. However, it remains unclear whether CMI moderates the relationship between medical staff allocation and medical expenditures. The present study focus on whether medical staff allocation predicts the cost per inpatient visit, whether this association is enhanced under conditions of high CMI. Aim: To explore the mechanism underlying the role of the CMI in the relationship between medical staff allocation and the average cost per inpatient visit. Methods: Data were collected from 207 general hospitals in Hubei Province in 2019 using the cluster sampling method. Pearson’s correlations were used to examine the associations between medical staff allocation, the CMI and the cost per inpatient visit. The moderating role of the CMI between medical staff allocation and the average cost per inpatient visit was analyzed using Model 1 of the PROCESS macro. Results: The number of patients per doctor ( r = −0.180, p < 0.01), beds per nurse ( r = −0.181, p < 0.01), beds per doctor ( r = −0.225, p < 0.01) and the average cost per inpatient visit showed a significant negative correlation. The CMI positively moderated the relationship between the average cost per inpatient visit and both the number of beds per doctor ( p < 0.01) and the number of beds per nurse ( p < 0.01). The moderating effect was stronger when hospitals had high CMI value influencing average cost per inpatient visit. Conclusion: CMI value can enhance the negative effects of the number of beds per doctor or nurse on average cost per inpatient visit, revealing that rational CMI benchmark for the hospital or prioritizing disease categories with clinically appropriate treatment complexity can mitigate the extent to which healthcare workforce allocation influences rising medical expenditures. Reasonable medical staff allocation and the CMI serve as important factors in controlling the average cost per inpatient visit.