Although migrant workers are a vulnerable group in China, they demonstrably contribute to the country's economic growth and prosperity. This study aimed to describe and assess the inequality of migrant worker health in China and its association with socioeconomic determinants. The data utilized in this study were obtained from the 2012 China Labor-force Dynamics Survey conducted in 29 Chinese provinces. This study converted the self-rated health of these migrant workers into a general cardinal ill-health score. Determinants associated with migrant worker health included but were not limited to age, marital status, income, and education, among other factors. Concentration index, concentration curve, and decomposition of the concentration index were employed to measure socioeconomic inequality in migrant workers' health. Prorich inequality was found in the health of migrant workers. The concentration index was -0.0866, as a score indicator of ill health. Decomposition of the concentration index revealed that the factors most contributing to the observed inequality were income, followed by gender, age, marital status, and smoking history. It is generally known that there is an unequal socioeconomic distribution of migrant worker health in China. In order to reduce the health inequality, the government should make a substantial effort to strengthen policy implementation in improving the income distribution for vulnerable groups. After this investigation, it is apparent that the findings we have made warrant further investigation.

Introduction: China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China.Methods: This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency.Results: The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran’s Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces’ values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume.Discussion: To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

Objective Analysing and evaluating how efficiently health resources are allocated to county-level Traditional Chinese Medicine (TCM) hospitals in Zhejiang Province, this study aims to provide empirical evidence for improving operational efficiency and optimising resource allocation in these hospitals. Design and setting The study employed a three-stage Data Envelopment Analysis (DEA) model to assess efficiency, using data from 68 county-level TCM hospitals. Four input and five output variables related to TCM services were selected for the analysis. Results The first-stage DEA results indicated that in 2022, the technical efficiency (TE) of TCM hospitals in Zhejiang Province was 0.788, the pure technical efficiency (PTE) was 0.876 and the scale efficiency (SE) was 0.903. The classification of hospitals into four groups based on the bed size showed statistically significant differences in returns to scale (p East (0.844) > South (0.805) > North (0.796) > Central (0.731). Conclusion There is an imbalance between the inputs and outputs of county-level TCM hospitals. Each region must consider factors such as the local economy, population and medical service levels, along with the specific development characteristics of hospitals, to reasonably determine the scale of county-level TCM hospital construction. Emphasis should be placed on improving hospital management and technical capabilities, coordinating regional development, promoting the rational allocation and efficient use of TCM resources and enhancing the efficiency of resource allocation in county-level TCM hospitals.

There were 245 million migrants in China in 2013, the majority of whom migrated from rural to urban areas. Thus, the purpose of this study was to investigate the association between sociodemographic, psychosocial, and lifestyle factors, and self-reported health (SRH) in Chinese migrant laborers. This study was conducted based on data from the China Labor-force Dynamics Survey 2012. SRH was measured in a single item, although there were other risk factors from three different groups: sociodemographic, psychosocial, and lifestyle factors. The associations between these risk factors and SRH were tested using multilevel logistic regression analyses including interaction tests. All three groups of factors were explored simultaneously. These factors included age, working hours, marital status, illness, and hospitalization, which were associated with poor SRH, as well as earnings, number of friends, relations with neighbors, trust level, education, and alcohol consumption, which were associated with good SRH. However, there was minimal association found between the two factors of medical insurance and nationality, and SRH. Our investigation indicated that there are many factors associated with SRH. In particular, this study undertook a comprehensive investigation of the associations between sociodemographic, psychosocial, lifestyle factors, and SRH in China, the results of which could better inform medical researchers and governments from a Chinese perspective.

Purpose: This study aimed to analyze the status of patient satisfaction in outpatients of tertiary hospitals and the factors affecting patient satisfaction, in order to provide a scientific basis for improving patient satisfaction. Methods: A total of 6480 surveys of outpatients were conducted by a cross-sectional study in 16 tertiary hospitals in the Zhejiang province of China. The main contents of the survey were the basic characteristics of patients. Statistical description, single-factor analysis and binary logistic regression analysis were used to screen influencing factors. Results: Results of this study showed that the total satisfaction score of outpatients was 87.13± 13.47, and higher scored factors in the survey factors were nursing level, the convenience of registration and convenience of appointment diagnosis and treatment. The factors with lower scores were treatment effect, environmental sanitation and comfort and other staffs’ attitudes. Hospital managers should pay attention to the improvement of treatment level, environmental sanitation and comfort and other staffs’ attitudes. Conclusion: In the process of serving outpatients, doctors should pay more attention to patients who are male, 31– 45 years old or over 60 years old, permanent residents, from public institutions, possessed postgraduate education, without medical insurance, and who visiting paediatrics and Chinese medicine hospitals.

Background: The Diagnosis-Intervention Packet Payment (DIP) system is regarded as a localized cost-control strategy in China. It aims to improve healthcare efficiency, curb the growth of medical expenses, and optimize the allocation of medical resources among diverse groups.Objective: This study aims to assess the impact of DIP payment reforms on differential changes in patients’ hospitalization expense and to explore the degree of concentration of hospitalization expense for patients with different insurance schemes undergoing treatment for typical diseases, with a view to providing policy recommendations for improving the medical insurance system.Methods: Data were collected from patients with cerebral infarction (CI) and coronary atherosclerotic heart disease (CAD) treated at primary, secondary, and tertiary hospitals in S City of China, from 2020 to 2023. Patients were classified into the Urban Employees’ Basic Medical Insurance (UEBMI) group and the Urban and Rural Residents’ Medical Insurance (URRMI) group based on two health insurance schemes. Propensity Score Matching (PSM) was employed to ensure a balanced sample. The changes and trends in hospitalization expenses across different groups were analyzed using the interquartile Range (IQR), standard deviation (SD), and concentration index.Results: Post-DIP reform, hospitalization expenses for patients with different diseases at various levels of hospitals have decreased annually. Regarding expenses variation, the standard deviation (SD) of hospitalization expenses for both UEBMI and URRMI exhibited a downward trend, with a decrease in the double-difference value each year. From the perspective of expenses concentration, all concentration indices were less than 0 (statistically significant, p < 0.01), indicating a higher concentration in hospitalization expenses for UEBMI.Conclusion: The DIP reform can effectively increase the concentration of hospitalization expenses, reduce the variability of changes in hospitalization expenses for both UEBMI and URRMI, and drive medical practices toward standardization and consistency. However, the degree of this expense reduction varies among the hospitals at all levels.