To analyze the trends and associated factors of complementary feeding practices (CFPs) among infants and young children (IYC) aged 6-23 months in rural areas of Liuzhou from 2017 to 2024.

Children nutrition improvement program monitoring data of 5 020 IYC in Liuzhou between 2017 and 2024 were retrospectively analyzed. The trends of CFPs were analyzed by trend χ²test, and the influencing factors of CFPs delivery by logistic regression analysis.

The prevalence of minimum dietary diversity (MDD), minimum meal frequency (MMF), minimum acceptable diet (MAD), egg and/or flesh food consumption (EFF), and zero vegetable or fruit consumption (ZVF) were 64.40%, 82.45%, 53.17%, 90.86%, and 15.14%, respectively. The prevalence of MDD and MAD decreased from 71.92% and 53.82% to 59.17% and 49.70% over the 8 year period, respectively (, 11.912, all P<0.01). The prevalence of MMF and ZVF increased from 77.20% and 9.75% in 2017 to 82.01% and 16.33% in 2024, respectively (, 33.036, all P<0.01). Multivariate logistic regression analysis showed that children of mothers with Miao ethnic group were negatively associated with MMF (OR=1.38, 95% CI: 1.06-1.79) and MAD (OR=1.37, 95% CI: 1.11-1.68). Mothers of senior high school/technical secondary school and above education were positively associated with MDD (OR=0.72, 95% CI: 0.58-0.88), MMF (OR=0.70, 95% CI: 0.55-0.90), and MAD (OR=0.70, 95% CI: 0.57-0.87). Fathers with Miao ethnic group were negatively associated with MMF (OR=2.04, 95% CI: 1.56-2.67) , and positively associated with ZVF (OR=1.44, 95% CI: 1.16-1.80). Fathers with Dong ethnic group were negatively associated with MMF (OR=1.84, 95% CI: 1.43-2.36). Employed fathers showed a significant association with a higher prevalence of MDD (OR=0.86, 95% CI: 0.74-0.99), MAD (OR=0.78, 95% CI: 0.68-0.90) and EFF (OR=0.70, 95% CI: 0.56-0.88), and lower prevalence of ZVF (OR=0.62, 95% CI: 0.52-0.74). The score for caregiver’s knowledge of feeding and nutrition were positively associated with MDD (OR=0.94, 95% CI: 0.89-0.98), MMF (OR=0.86, 95% CI: 0.80-0.91), MAD (OR=0.86, 95% CI: 0.82-0.90), and EFF (OR=0.89, 95% CI: 0.82-0.97). Children aged 12-23 months were more likely to have MDD (OR=0.35, 95% CI: 0.30-0.41), MAD (OR=0.59, 95% CI: 0.51-0.69), and EFF (OR=0.13, 95% CI: 0.10-0.16) but less likely to have ZVF (OR=0.22, 95% CI: 0.18-0.26). Children with a birth order of being third or greater were negatively associated with MAD (OR=1.26, 95% CI: 1.06-1.50). Multiple children were positively associated with MMF (OR=0.28, 95% CI: 0.14-0.58) and MAD (OR=0.58, 95% CI: 0.41-0.83). Children with diarrhea in the last 2 weeks were positively associated with MMF (OR=0.45, 95% CI: 0.26-0.76). Taking nutrient supplements were positively associated with MDD (OR=0.82, 95% CI: 0.71-0.94), MMF (OR=0.82, 95% CI: 0.68-0.98) and EFF (OR=0.72, 95% CI: 0.58-0.90).

Improvement in CFPs among children aged 6-23 months in rural areas of Liuzhou is unsatisfactory, specifically MDD and MAD. Attention needs to be paid to ethnic minorities, mothers with low education, fathers without job, children with young age and high birth order, and strengthening caregiver’ education on IYC feeding practices, in order to improve CFPs.

To analyze the impact of nighttime social media useon pregnancy-specific anxiety among pregnant women, to provide evidence to promote mental health during pregnancy.

Ainvestigation was completedamong pregnant women undergoing prenatal examinations at Qilu Hospital of Shandong University from December 2024 to March 2025 using instruments including self-made general information questionnaire, Nighttime-useSocial Media Scaleand Pregnancy-specific Anxiety Questionnaire (PAQ). Latent profile analysis was used to explore the latent classes of nighttime social media use, while logistic regression analysis was used to analyze the relationship between its latent profiles and pregnancy-specific anxiety.

A total of 855 pregnant women were included, the average score of nighttime social media use was 19.54±4.38, which could be divided into three latent classes including low-frequency use with good sleep (41.2%), high-frequency use with delayed sleep (49.2%), and high-frequency use with disrupted sleep (9.6%). Nighttime social media use significantly increased the risk of pregnancy-specific anxiety (P<0.001). The high-frequency use with delayedsleep group (OR=2.903, 95% CI=2.118-3.979) and the high-frequency use with disruptedsleep group (OR=5.087, 95% CI=2.771-9.339) had a higher risk of pregnancy-specific anxiety compared with the low-frequency use with good sleep group (P<0.001).

Nighttime social mediause is prevalent among pregnant women with significant inter-individual heterogeneity and increases the risk of pregnancy-specific anxiety. Particular emphasis should be placed on the impacts of nighttime social media use in pregnant woman, with targeted interventions implemented to promote physical and mental health during pregnancy.

To investigate the dynamic trajectories of cognitive function and its influencing factors among elderly populations relocated through poverty alleviation resettlement programs.

A multistage stratified sampling approach was used to select 740 elderly individuals from 24 resettlement sites across four cities and eight counties in Shanxi Province for three rounds of follow-up surveys. Latent growth mixture models (LGMM) identified cognitive trajectory categories, and multivariable logistic regression analyzed influencing factors.

The prevalence of cognitive impairment was 22.6%, with an overall upward trend in cognitive scores. LGMM identified two distinct trajectories: a “Peak-high cognitive function group” and a “Valley-low cognitive function group.” Advanced age (OR=2.914, 95% CI: 1.946-4.362), non-marital status (OR=2.551, 95% CI: 1.632-3.985), lack of regular physical exercise (OR=1.952, 95% CI: 1.327-2.873), and obesity (OR=2.652, 95% CI: 1.579-4.452) were significant risk factors for the low cognitive trajectory, while better basic functional ability was a protective factor (OR=0.723, 95% CI: 0.637-0.821).

While cognitive function showed overall improvement in the relocated elderly population, specific risk factors significantly increased the risk of cognitive decline. Recommendations include promoting early cognitive screening, expanding community sports facilities, and implementing family-based social support programs.

To construct the Compliance Scale for primary prevention of stroke among the elderly in the community, to evaluate the compliance level of the elderly in the community as a whole, and to provide reference tools for the investigation of the current situation of primary prevention, influencing factors and health education.

In accordance with the 2024 Guideline for the Primary Prevention of Stroke from the American Heart Association/American Stroke Association, the theory of self-care for chronic diseases was used as the theoretical framework. Literature review and Delphi method were used to conduct two rounds of consultation with 18 experts. A pilot study was conducted among community-dwelling older adults to validate the applicability of the scale items.

The questionnaire recovery rate for both rounds of inquiry reached 100%, and the expert authority coefficient was 0.89. In the two rounds of correspondence, the average importance of items was >3.5, the full score ratio was >0.2, the CV (coefficient of variation) stood at ≤0.25, and the Kendall W coordination coefficient was 0.305 and 0.265 respectively (all P<0.001). The final form is a community-based primary prevention behavior compliance scale for elderly stroke with 3 dimensions, 10 sub dimensions and 33 items.

Based on the guidelines for primary prevention of stroke (2024) and the theory of self-care for chronic diseases as the theoretical framework, the community elderly stroke primary prevention behavior compliance scale has good reliability and scientificity, and can be used as an evaluation tool to evaluate the community elderly stroke primary prevention behavior compliance.

To optimize medical cost control, enhance disease prevention systems, and reform healthcare payment models to investigate hospital care status, cost components, and influence factors for ischemic stroke patients in Changzhi, Shanxi.

Based on the hospitalization data of 90 465 patients with ischemic stroke in Changzhi from 2018 to 2023, a structural equation model was constructed to analyze the factors affecting hospitalization costs.

Ischemic stroke hospitalizations increase annually (males faster), with the 60-69 age group being predominant and onset age shifting older. The average length of stay is 11.58 days (decreasing), and the proportion of critical/urgent admissions rises, indicating increased disease severity. More patients undergo clinical pathways, with more cured/improved cases (suggesting medical advances). Patients with ≥2 admissions and those visiting tertiary hospitals are increasingyearly. The average hospitalization cost is 8 965.01, dominated by drug and diagnostic fees; while average cost, comprehensive service, treatment, and rehabilitation fees rise, diagnostic and drug costs decline. SEM analysis ranks influencing factors as: length of stay>hospital level>age>gender>number of admissions>clinical pathway implementation>condition at admission>medical insurance type.

Although initial progress in cost control is seen, patients’ economic burden remains heavy. It is necessary to optimize cost structure and hospitalization cycle management, focus on influencing factors, and strengthen whole-cycle disease prevention and control.

To examine the current status of public health responsibility development in public hospitals in Jiangxi Province and to propose targeted strategies.

A stratified random sampling approach was applied to select 271 hospitals across Jiangxi Province. A questionnaire survey was conducted covering eight dimensions, including disease prevention and control-public health collaboration. Descriptive analysis and chi-square tests were employed to assess the status of public health responsibility development in public hospitals and to compare differences across hospital levels.

A total of 258 hospitals were included in the study. Among them, 94.57% provided basic public health services, 89.15% had established independent public health departments, but only 49.22% had dedicated staff with a public health background. Significant differences were observed across hospital levels. Tertiary hospitals performed significantly better in establishing surveillance and early warning systems (100%), departments for infectious diseases (98.25%), and intelligent monitoring (84.21%) (P<0.05). Primary hospitals performed better in the coverage of basic public health services (100%) and public health performance evaluation (91.74%). In contrast, secondary hospitals lagged behind in establishing independent public health departments and staff training. General hospitals demonstrated overall superiority compared with non-general hospitals.

There are significant level-based differences in the implementation of public health functions in public hospitals in Jiangxi Province, requiring tailored strategies for each level.

To construct a set of scientific, systematic and specialty-specific operation evaluation index system for tertiary public psychiatric hospitals, and to provide tools and references for hospital refined management and health policy formulation.

Based on the balanced scorecard theory, the index pool was formed through policy analysis, literature research and semi-structured interviews, the final index system was determined by Delphi method, and the index weight was calculated by analytic hierarchy process.

After two rounds of expert consultation (the positive coefficient of experts reached 100%, the authority coefficient was 0.86-0.87, and Kendall’s W test was significant), the evaluation system consisting of 4 first-level indexes, 13 second-level indexes and 43 third-level indexes was finally established. The weight analysis showed that the patient dimension had the highest weight among the first-level indicators (0.466 8), followed by the financial dimension (0.277 6), highlighting the “patient-centered” service orientation; Among the three-level indicators, patient satisfaction and waiting time after outpatient appointment had the highest weight.

This index system effectively solves the problem of “acclimatization” of general evaluation standards in the field of psychiatry, is an important tool to promote high-quality connotative development, and has reference value for performance appraisal and policy improvement in the field of mental health.

To systematically analyze the overall architecture, evolution, and core issues of medical data trading policies in China, identify deficiencies within the policy system, and to provide a basis for subsequent policy optimization.

Using NVivo 12 Plus software, we conducted content mining and thematic coding of 48 policy texts related to medical data trading issued at the national level between 2015 and 2025. A structural model of policy elements was constructed, and a combined qualitative and quantitative analysis was performed.

Medical data trading policies in China have formed a three-tier governance structure of “top level design-institutional mechanisms-development pathways.” However, the system exhibits significant structural imbalances, including a fragmented framework and lack of industry-specific legislation at the top-level design tier; a dual weakness in operational safety rules and supplier incentives at the institutional mechanism tier; and lagging development of infrastructure and the professional service ecosystem at the development pathway tier.

The policy system should be improved by strengthening top-level design through industry-specific legislation, improving institutional mechanisms with operational safety rules and incentive schemes, and bolstering development pathways by fostering a professional service ecosystem. These measures aim to enhance the efficiency and security of medical data factor market allocation, thereby supporting the deep integration of the “Healthy China” and “Digital China” strategies.

To explore the correlation between digital literacy and the depression trajectory of the elderly in China.

Based on data from the three waves of the China Family Panel Studies (CFPS) from 2016 to 2020, this study applied a group-based trajectory model to construct the changing trajectory of depression among the elderly, and employed a multi-class logistic regression model to investigate the association between digital literacy and the depression trajectory.

Three trajectory groups were identified: “long-term non-depression group” (64.87%), “depression slow-rising group” (29.75%), and “long-term high depression group” (5.38%). Compared with elderly individuals in the long-term non-depression group, the possibility that the depression trajectory belonged to the depression slow-rising group (RRR=0.905, 95% CI: 0.851-0.962) and the long-term high depression group (RRR=0.681, 95% CI: 0.510-0.919) was smaller if the digital literacy was higher.

There is heterogeneity in the depression trajectories of older adults, and digital literacy significantly influences these trajectories. The digital literacy of the elderly population should be enhanced to alleviate their depression.

To investigate the separate associations of cumulative ACEs exposure and specific ACEs patterns with cognitive function among middle-aged and older adults.

Using data from the China Health and Retirement Longitudinal Study (2011-2020), 12 ACE indicators were included. Linear mixed-effects model was employed to analyze the longitudinal effects of the cumulative number of ACEs and ACE patterns on cognitive function, with ACEs patterns identified using latent class analysis.

Compared with those who did not experience ACEs, those who experienced ≥4 ACEs had a faster decline in cognitive function (β=-0.029, 95% CI: -0.045--0.012). Latent class analysis identified four ACE patterns: low ACEs, domestic abuse and bullying, emotional and physical neglect, and parental mental illness and death. Among these, the “parental mental illness and death” subgroup experienced faster cognitive decline than the low ACEs group (β=-0.018, 95% CI: -0.027--0.008).

Both cumulative ACEs exposure and specific adversity patterns are associated with accelerated cognitive decline in middle-aged and older adults. Individuals who have experienced ≥4 ACEs and parental mental illness and death require particular attention.