Background: China’s fragmentation of social health insurance schemes has become a key obstacle that hampers equal access to health care and financial protection. This study aims to explores if the policy intervention Urban and Rural Residents Basic Medical Insurance (URRBMI) scheme, which integrates Urban Resident Basic Medical Insurance (URBMI) and New Rural Cooperative Medical Scheme (NCMS), can curb the persistent inequity of catastrophic health expenditure (CHE) and further analyses the determinants causing inequity. Methods: Data were derived from the Fifth National Health Service Survey (NHSS). A total of 11,104 households covered by URRBMI and 20,590 households covered by URBMI or NCMS were selected to analyze CHE and the impoverishment rate from medical expenses. Moreover, the decomposition method based on a probit model was employed to analyse factors contributing CHE inequity. Results: The overall incidence of CHE under integrated insurance scheme was 15.53%, about 1.10% higher than the non-integrated scheme; however, the intensity of CHE and impoverishment among the poorest was improved. Although CHE was still concentrated among the poor under URRBMI (CI = -0.53), it showed 28.38% lower in the degree of inequity. For URRBMI households, due to the promotion of integration reform to the utilization of rural residents’ better health services, the factor of residence (24.41%) turns out to be a major factor in increasing inequity, the factor of households with hospitalized members (− 84.53%) played a positive role in reducing inequity and factors related to social economic status also contributed significantly in increasing inequity. Conclusion: The progress made in the integrated URRBMI on CHE equity deserves recognition, even though it did not reduce the overall CHE or the impoverishment rate effectively. Therefore, for enhanced equity, more targeted solutions should be considered, such as promoting more precise insurance intervention for the most vulnerable population and including costly diseases suitable for outpatient treatment into benefit packages. Additionally, comprehensive strategies such as favourable targeted benefit packages or job creation are required for the disadvantaged.

Objective: Elderly patients with type 2 diabetes mellitus are highly vulnerable due to severe complications. However, there is a contradiction in the relationship between social support and quality of life, which warrants further exploration of the internal mechanism. This study assessed the quality of life and its interfering factors in this patient population. Methods: In total, 571 patients with type 2 diabetes mellitus over 60 years old were recruited from two community clinics in Heilongjiang Province, China. We collected data on health status, quality of life, self-management behavior, fasting plasma glucose (FPG) level, and social support. Structural equation modeling and the bootstrap method were used to analyze the data. Results: The average quality of life score was − 29.25 ± 24.41. Poorly scored domains of quality of life were “Psychological feeling” (− 8.67), “Activity” (− 6.36), and “Emotion” (− 6.12). Of the 571 patients, 65.32% had normal FPG, 9.8% had high-risk FPG, 15.94% had good self-management behavior, and 22.07% had poor social support. Significant correlations among social support, self-management behavior, FPG level, and quality of life were noted. A multiple mediator model revealed that social support influenced quality of life in three ways: (1) directly (c′ = 0.6831); (2) indirectly through self-management behavior (a1*b1 = 0.1773); and (3) indirectly through FPG control (a2*b2 = 0.1929). Self-management behavior influenced the quality of life directly and indirectly through FPG control. Conclusion: Improving self-management behavior and monitoring hypoglycemia should become priority targets for future intervention. Scheduled social support to self-management projects should be put into the standardized management procedure. Physicians should provide substantial and individualized support to the elderly patients with type 2 diabetes mellitus regarding medication, blood glucose monitoring, and physical exercise.

Objective: This study aimed to assess Chinese public pandemic fatigue and potential influencing factors using an appropriate tool and provide suggestions to relieve this fatigue.Methods: This study used a stratified sampling method by age and region and conducted a cross-sectional questionnaire survey of citizens in Xi'an, China, from January to February 2022. A total of 1500 participants completed the questionnaire, which collected data on demographics, health status, coronavirus disease 2019 (COVID-19) stressors, pandemic fatigue, COVID-19 fear, COVID-19 anxiety, personal resiliency, social support, community resilience, and knowledge, attitude, and practice toward COVID-19. Ultimately, 1354 valid questionnaires were collected, with a response rate of 90.0%. A binary logistic regression model was used to examine associations between pandemic fatigue and various factors.Result: Nearly half of the participants reported pandemic fatigue, the major manifestation of which was “being sick of hearing about COVID-19” (3.353 ± 1.954). The logistic regression model indicated that COVID-19 fear (OR = 2.392, 95% CI = 1.804–3.172), sex (OR = 1.377, 95% CI = 1.077–1.761), the pandemic's impact on employment (OR = 1.161, 95% CI = 1.016–1.327), and COVID-19 anxiety (OR = 1.030, 95% CI = 1.010–1.051) were positively associated with pandemic fatigue. Conversely, COVID-19 knowledge (OR = 0.894, 95% CI = 0.837–0.956), COVID-19 attitude (OR = 0.866, 95% CI = 0.827–0.907), COVID-19 practice (OR = 0.943, 95% CI = 0.914–0.972), community resiliency (OR = 0.978, 95% CI = 0.958–0.999), and health status (OR = 0.982, 95% CI = 0.971–0.992) were negatively associated with pandemic fatigue.Conclusion: The prevalence of pandemic fatigue among the Chinese public was prominent. COVID-19 fear and COVID-19 attitude were the strongest risk factors and protective factors, respectively. These results indicated that the government should carefully utilize multi-channel promotion of anti-pandemic policies and knowledge.

This study aims to explore key factors and identify root factors influencing the Public Health Emergency Response Mechanism (PHERM) to ensure timely and effective responses to emerging infectious disease crises and enhance the efficiency of emergency operations. We employed a mixed-method approach using DEMATEL-ISM-MICMAC to analyse the interrelationships among factors affecting PHERM. The DEMATEL method established the hierarchical structure of the factors, ISM determined the relational paths, and MICMAC further characterized the attributes of the factors. The analysis revealed that PHERM's influencing factors are organized into four levels, with the conscientiousness of emergency leadership action (X15) identified as the most profound and influential factor, exhibiting a strong causality with a high driving force. The decision-making and command feedback capability (X8) emerged as a significant outcome factor in the transition layer, highly influenced by other factors and with the highest node degree. The proactive emergency response awareness and actions of leaders is crucial for the mechanism's smooth and efficient operation. It is essential to prioritise ideological education and simulation training to instill such awareness. Moreover, proactive preparation for factors associated with decision-making and command capabilities is necessary to mitigate potential hesitation and panic during actual epidemic prevention, thereby enhancing the operational effectiveness of PHERM.

We aimed to evaluate the global burden and trends of lower respiratory infections (LRIs) attributable to non-optimal temperatures between 1990 and 2021, focusing on age, period, and cohort effects as well as health inequalities to inform targeted public health policies. Using the Global Burden of Disease 2021 database, we obtained the age-standardized mortality rate (ASMR) and disability-adjusted life-years rate (ASDR) for LRIs related to non-optimal temperatures. We calculated estimated annual percentage changes (EAPC) to assess LRIs burden trends and applied age-period-cohort modeling to quantify age, period, and cohort effects. Health inequalities were evaluated using the slope index of inequality and the concentration index. In 2021, the highest ASDR for LRIs due to high temperatures occurred in children under 5 (347.66/100,000), whereas the highest ASMR for LRIs due to low temperatures occurred in adults aged ≥ 65 (338.49/100,000). Globally, the LRIs burden from non-optimal temperatures declined (EAPC: ASMR –2.48; ASDR –3.33). However, among the five climate zones, the LRIs burden in the boreal zone due to high temperatures increased (EAPC: ASMR 24.14; ASDR 45.14), whereas all other climate zones showed decreasing trends. In lower Sociodemographic Index (SDI) regions, the high-temperature–related LRIs burden was more pronounced. Relative inequities driven by non-optimal temperatures worsened in low-SDI regions. From 1990 to 2021, the global burden of LRIs attributable to non-optimal temperatures declined overall; however, high-temperature–related LRIs increased in boreal zones. These health inequalities underscore the urgent need for targeted climate adaptation policies, such as providing international assistance, improving infrastructure, offering healthcare resources, and promoting vaccine coverage, particularly for vulnerable populations in low-SDI regions and boreal zones.

The COVID-19 pandemic has highlighted the continued inadequacy of the capability of Center for Disease Control and Prevention (CDC) public health personnel to respond effectively to emerging infectious disease outbreaks, as well as the urgent need for specific tools to assess the capability needed by CDC public health personnel in the post-pandemic era. Using network analysis, we aimed to analyze the comprehensive capacities of CDC public health staff and the relationships between these capacities. We also examined the impact of standardized public health training on their capacities and provided actionable recommendations for improving their training and overall capability. This study employs a cross-sectional design. A self-developed questionnaire was used to evaluate the capabilities of public health personnel in CDC. Network analysis was conducted using the qgraph package in R (version 4.3.1) to construct a capability network model, while the bootnet package ensured the stability and reliability of the network through bootstrapping. The NetworkComparisonTest package was applied to compare network structures and identify differences between groups. Over half (51.80%, N = 11,912) of public health personnel rated their comprehensive capabilities as poor. Core capabilities, including research, motivation, and emergency response, were identified as pivotal within the capability network. The network stability coefficient for strength was 0.75, indicating reliable results. The capability networks of those who participated in standardized training differed significantly from those who did not (P = 0.04). CDC public health personnel exhibit significant capability gaps, particularly in research and leadership. Standardized training provides some benefits but remains insufficient. Policymakers should address these gaps by aligning training content with critical capability needs, offering flexible and targeted training methods (e.g., virtual courses, self-paced modules), and implementing capability-based assessments to evaluate training outcomes.