Ping Luo

Background: Despite China’s significant reduction in maternal mortality rate (MMR), substantial inter-provincial disparities persist, reflecting uneven socioeconomic development and health system capacity. Previous studies have rarely examined the interplay of these factors using standardized inequality measures. This study quantifies maternal health inequalities across 31 Chinese provinces and identifies key socioeconomic and health system determinants. Methods: Using provincial panel data (2009–2021), we measured absolute and relative MMR disparities via the Slope Index of Inequality (SII) and Relative Index of Inequality (RII). Random-effects meta-regression models assessed how socioeconomic (population size, urbanization, college student numbers, total wealth, GDP per capita, insurance coverage) and health system variables (health workforce, beds, health expenditures per capita) influenced inequality, with stratification by eastern vs. central/western regions. Results: SII and RII varied considerably across provinces, indicating significant heterogeneity. Total wealth and higher education levels consistently reduced inequality, especially in central/western regions. In contrast, larger population size, urbanization, and uneven workforce distribution were associated with greater disparities. Health insurance had a protective effect mainly in less-developed areas. Paradoxically, higher health workforce density and rapid urbanization correlated with increased inequality, suggesting structural inefficiencies and unequal resource concentration. Conclusion: Household income, education, and insurance coverage are crucial for reducing maternal health disparities, whereas uneven urbanization and workforce allocation exacerbate them. Region-specific governance—focusing on fiscal transfers, education investment, and equitable health workforce distribution in central/western provinces, alongside efficiency improvements in the east—is essential for achieving Healthy China 2030 and SDG 3.1. Future policies must prioritize equity alongside average improvements.

This paper elucidates the prerequisites for maximum likelihood estimation (MLE) of parameters within the exponential and scale parameter families. Estimation of these parameters is predicated on data derived from censored samples and seeks to adhere to stochastic ordering principles. The study establishes that for two independent normal distributions and a two-parameter exponential distribution discernible by the distinct parameter sets, the MLEs of the parameters evince a stochastically ordered relationship when evaluated using full datasets. Furthermore, this research is extended to corroborate the persistence of stochastic ordering in the MLEs of such parameters under conditions of fixed censoring of samples.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.