To analyze the death status and life loss of patients with severe mental disorders in Wuhan during the period from 2018 to 2022, in order to provide references for formulating bailout management policy for patients with severe mental disorders.

The death data on gender, age, diagnosis, cause of death, and other related information were obtained from the National Information System for Severe Mental Disorders during 2018—2022. Excel 2022 and SPSS 21.0 software were applied to analyze the death characteristics, death cause distribution and mortality. Years of life lost (YLL) and related indicators were used to analyze the life expectancy reduction due to severe mental disorders.

From 2018 to 2022, a total of 3 557 patients with severe mental disorders died in Wuhan, most of them had schizophrenia (2 541 cases, 71.44%). There were more male patients (2 019 cases, 56.76%) than female patients (1 538 cases, 43.24%). The average age of death was 58.06±15.21 years, mainly concentrated in the elderly population over 60 years old (1 719 cases, 48.33%). Significant differences in mortality rates were observed between 2018 and 2022(χ²=58.678, P<0.001), with statistically significant differences found only among patients with schizophrenia (χ²=45.600, P<0.001) and those with mental retardation accompanied by mental disorders (χ²=16.120, P=0.003). Physical diseases were the leading cause of death (2 193 cases, 61.65%). For patients aged 1-69 years, all causes of death resulted in 83 380.75 person-years of potential years of life lost (PYLL), an average years of life lost (AYLL) of 30.71 years, and a potential years of life lost rate (PYLLR) of 173.89%. The greatest total loss of life was caused by schizophrenia, while the highest AYLL was observed for epilepsy with mental disorders (34.08 years).

Severe mental disorders are important diseases that cause life loss, and combined physical diseases are the main cause of death for patients in Wuhan from 2018 to 2022. It is suggested that we should pay more attention to chronic disease management of patients with severe mental disorders, strengthen bailout, and take multiple measures to reduce the risk of death of patients.

To investigate the prevalence of seasonal human coronaviruses (sHCoV) in acute respiratory infection cases in selected medical institutions in Shanghai before and after the COVID-19 pandemic.

A retrospective analysis was conducted on respiratory samples from 11 794 acute respiratory infection cases collected between January 2016 and December 2023 at nine medical institutions. Multiplex PCR was used to detect sHCoV, and the χ² tests were performed to compare detection rates across three periods (pre-pandemic, during the pandemic, and post-pandemic) and to assess changes in epidemic characteristics.

Among the 11 794 respiratory samples, the overall detection rate of eight common respiratory viruses declined significantly during and after the pandemic. The overall detection rate of sHCoV was 3.47% (409/11 794), decreasing to 1.58% (55/2 259) during the pandemic and further to 1.42% (32/3 482) post-pandemic (P<0.001). Before the pandemic, sHCoV circulated year-round, with peak activity from March to September. During the pandemic, the peak shifted to July-December, whereas post-pandemic, detections were observed throughout the year without a clear seasonal peak. Before the pandemic, the four sHCoV subtypes circulated alternately, with HCoV-NL63 being the most prevalent and remaining stable during the pandemic. However, post-pandemic, HCoV-229E became dominant, and subtype-specific trends shifted. Notably, HCoV-229E transitioned from a biphasic to a monophasic pattern, and re-emerging with a biphasic trend post-pandemic. The peak circulation periods of HCoV-HKU1 and HCoV-OC43 were delayed by 5-8 months, whereas HCoV-NL63 was not detected in the post-pandemic period.

The COVID-19 pandemic may have influenced the transmission patterns of sHCoV. After the pandemic, there was a decrease in the detection rate of sHCoV, a delayed seasonal peak, and changes in the viral subtype composition. Continued monitoring of these trends is essential to provide scientific support for respiratory infection prevention and control strategies.

To investigate the relationships between different dimensions of biological rhythm disorders, depression and anxiety symptoms among adolescents, and to identify the key dimensions that have a greater impact on mental health, providing a basis for developing targeted interventions.

Conducted from September to November 2023 in Tianjin, this study recruited middle school students from urban and suburban areas using convenience and stratified cluster sampling methods, yielding 3 787 valid questionnaires. The Self-rating Questionnaire of Biological Rhythm Disorders for Adolescents (SQBRDA), the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7) were employed to assess biological rhythm disorders, depression, and anxiety symptoms, respectively. Network analysis was utilized to examine the association strengths between dimensions and to calculate centrality indices to pinpoint key nodes.

Activity rhythms showed higher centrality in the network (0.436), the edge weight between decreased interest and low mood was the largest in the depression symptom subnetwork (0.319), and the edge weight between persistent worry and excessive worry was the largest in the anxiety symptom subnetwork (0.446). In the biological rhythm subnetwork, the edge weight between activity rhythms and electronic product use rhythms was the largest (0.566). Females had stronger associations in the depression symptom network and the biological rhythm disorder network (0.194).

The study demonstrates a complex correlation network between adolescent biological rhythm disorders, depression and anxiety symptoms, especially activity rhythm and electronic product use rhythm.

To construct a prediction model for metabolic syndrome (Metabolic Syndrome, MetS) in railway employees based on machine learning algorithms (Machine Learning, ML) and evaluate the prediction performance.

The time to the onset of metabolic syndrome was used as the outcome variable, with demographic characteristics and biochemical indicators as predictive variables. Univariate analysis was conducted to select predictive indicators. The study subjects were randomly divided into a training set and a test set in a 7:3 ratio. Cox proportional hazards regression, Random Forest (Random Survival Forest, RSF), and Gradient Boosting Machine (Gradient Boosting Machine, GBM) were used to build metabolic syndrome prediction models. Model performance was assessed using the area under the receiver operating characteristic curve (Area under curve, AUC), concordance index (C-index), sensitivity, specificity, accuracy, and F1 score. A risk calculator was created using the shiny package.

This study included 17 087 subjects and collected 28 indicators. Univariate analysis identified 22 statistically significant indicators. In the training set, the areas under the curve (area under the curve, AUC) of the prediction models constructed by Cox, RSF, and GBM were 0.870,0.938, and 0.891, respectively; C-index values were 0.853,0.935, and 0.843; sensitivity was 0.612,0.968, and 0.628; specificity was 0.933,0.742, and 0.994; accuracy was 0.678,0.788, and 0.703; F1 scores were 0.751,0.839, and 0.749.

The RSF model outperformed the Cox model and the GBM model in predicting metabolic syndrome among railway employees, providing a scientific basis for early identification of metabolic syndrome and aiding in the implementation of primary prevention measures.

To analyze the tobacco epidemic status among residents in Nanshan District, Shenzhen, evaluate the effectiveness of tobacco control, and propose improvement strategies.

A multistage sampling method was used to select 1 969 households across the district for home visits and surveys. Weighted data analysis was performed using R 4.3.3 software.

The current smoking rate among residents in Nanshan District, Shenzhen, was 13.56% (95%CI: 10.01%-17.72%), which was lower than the level of Shenzhen City in 2022 (19.07%), with males having a higher rate than females (, P<0.01). The current use rate of e-cigarettes was 1.98%, with the age range concentrated between 25 and 44 years old, and the rate in males was higher than that in females (, P<0.05). The smoking cessation rate was 19.52% (95%CI: 11.69%-29.37%). The smoking cessation attempt rate was 55.01% (95%CI: 41.57%-67.98%). The main quitting method was dry quitting, 22.09% (95%CI: 14.66%-30.96%). The exposure rate to second-hand smoke was 57.17% (95%CI: 49.89%-64.25%). Among places with second-hand smoke exposure, the exposure rate in restaurants was the highest (33.23%) (95%CI: 28.46%-38.25%). In terms of awareness of the harms of smoking, 32.73% knew that smoking can cause impotence, and only 28.38% were aware of all four diseases (stroke, heart disease, lung cancer, and impotence) caused by smoking. The awareness rate of all three diseases (adult heart disease, children’s lung diseases, and adult lung cancer) caused by second-hand smoke exposure was 43.62%.

The smoking rate among residents in Nanshan District, Shenzhen City has been effectively controlled, but challenges persist, including youth-oriented e-cigarette use, severe second-hand smoke exposure, and insufficient awareness of tobacco-related harms. Strengthening enforcement of smoking bans in public spaces, improving smoking cessation services, and enhancing targeted health education are urgently needed.

Regarding the effect of alcohol intake on the risk of gout, existing studies have shown a diversity of conclusions. Therefore, we conducted Mendelian Randomization (MR) analysis to assess the causal association between various types of alcohol intake and the risk of gout.

Instrumental Variables (IVs) were obtained from Genome-Wide Association Study (GWAS) databases. We used the TwoSampleMR package in R to perform MR analysis with the Inverse Variance Weighting (IVW) method. The Cochran Q test was employed to assess heterogeneity, and the intercept and P-value from MR-Egger regression were used to test and adjust for horizontal pleiotropy. Sensitivity analysis was conducted using the Leave-One-Out (LOO) method.

The MR analysis revealed a significant positive causal association between Beer/Cider Intake and Gout (logOR=1.187, 95%CI: 0.033 - 2.340, P=0.044). A significant negative causal association was found between White Wine Intake and Gout (logOR=-0.878,95%CI: -1.626--0.131, P=0.021). Similarly, Red Wine Consumption showed a significant negative causal association with gout (logOR=-6.514, 95%CI: -10.678--2.350, P=0.002). Fortified Wine Intake demonstrated a significant positive causal association with Gout (logOR=2.045,95%CI:0.097-3.994, P=0.040). When all types of alcohol were considered as a whole, a significant negative causal association with gout was observed (logOR=-1.462, 95%CI:-2.801--0.123, P=0.032). Spirits showed pleiotropy in relation to gout (P<0.05) and were therefore not included in the analysis.

The MR analysis results indicate that Beer/Cider Intake and Fortified Wine Intake may be a risk factor for the development of gout; Whereas White Wine Intake and Red Wine Consumption may reduce the incidence of gout. When all types of alcohol were considered as a whole, their consumption may reduce the risk of gout.

To explore the influencing factors of medical staff’s active reporting of adverse medical events by using Logistic regression and decision tree models, and to provide corresponding solutions.

A total of 811 medical workers in a tertiary hospital were investigated by random sampling. Logistic regression and decision tree model were used to analyze the factors of active reporting of medical adverse events by medical staff, and the area under ROC curve was calculated to compare and judge the analysis effect of the two models.

Only 55.1% of the medical staff in this hospital have voluntarily reported medical adverse events. The results of the two models showed that occupation, working years, knowledge of the reporting process of the hospital, and whether additional work would be added to the cumbersome reporting procedures were the influencing factors for the active reporting of medical staff (P<0.05). The AUC of Logistic regression model was greater than that of decision tree model, and the difference was statistically significant (Z=3.424, P<0.001).

The rate of active reporting of medical adverse events by medical staff in this hospital is relatively low. It is suggested that multiple measures be taken to promote the active reporting by medical staff.

To assess the level of health literacy on cancer prevention and control and its influencing factors among residents in Sichuan Province, and to provide a scientific basis for optimizing health education strategies on cancer prevention and control.

In April 2023, a convenience sampling method was used to recruit 28 163 permanent residents aged 15-69 from 21 cities (prefectures) in Sichuan Province for a cross-sectional survey. The χ² test was used to analyze the differences among different groups, and the multivariate Logistic regression model was employed to explore the influencing factors of cancer prevention and control literacy.

After weighted adjustment, the overall literacy level of cancer prevention and control among residents in Sichuan Province was 45.16%. There were differences in the literacy levels of various dimensions. Specifically, the literacy levels for cancer awareness, cancer prevention, early diagnosis and treatment, cancer management, and cancer rehabilitation were 37.45%, 48.74%, 63.40%, 79.04%,and 39.22%, respectively. Multivariate logistic regression analysis revealed that residents aged 25-69, with an educational level of junior high school or above, working in government/public institutions, holding urban household registration, with an annual household income of ≥50 000 RMB, non-smokers, and with self-rated health statuses of poor, fair, or good were more likely to possess cancer prevention and control literacy. Conversely, residents with a household size of 4 or more, and those without a family history of cancer were less likely to possess such literacy. All these findings were statistically significant (P<0.05).

The literacy level among residents in Sichuan Province still needs to be improved, requiring enhanced public education on primary cancer prevention and rehabilitation management knowledge, with targeted awareness campaigns for key populations.

To explore the impact of grip strength asymmetry on cognitive function among elderly people in Chengdu, to investigate the mediating role of activities of daily living (ADL) between grip strength asymmetry and cognitive function, and to provide scientific evidence for reducing the risk of cognitive decline.

This study analyzed survey data from Wenjiang District and Longquanyi District in Chengdu in 2023. Handgrip strength asymmetry was calculated from bilateral grip strength measurements, and cognitive function was assessed using the Minimum Mental State Examination (MMSE). Correlation analysis and regression analysis were used to explore the relationship between handgrip strength asymmetry, ADL and cognitive function in the elderly. Bootstrap tested the mediating role of ADL between handgrip strength asymmetry and cognitive function.

The study included 1 110 participants, including both urban and rural residents of Chengdu, with an average age of 72.38±5.82 years, including 660 (59.5%) women. The handgrip strength asymmetry was negatively correlated with cognitive function and ADL (r=-0.121, P<0.001; r=-0.138, P<0.001), ADL was positively correlated with cognitive function (r=0.125, P<0.001). The results of mediating effect test showed that ADL played a partial mediating role between the handgrip strength asymmetry and cognitive function (P<0.05), and the mediating effect accounted for 12.9%.

Our findings suggest that grip strength asymmetry can lead to impairment in activities of daily living, which in turn causes cognitive decline among the elderly population in Chengdu. Thus, ADL plays a partial mediating role between handgrip strength asymmetry and cognitive function. Physical exercise targeting grip strength and ADL may be an effective approach to preventing and delaying cognitive decline.

To investigate the impact of chemical components in multi-size particulate matter on mortality risk from circulatory system diseases, and to provide evidence for establishing refined air pollution control strategies.

Based on mortality data for circulatory system diseases in Tianjin from 2019 to 2022, integrated with multi-size particulate matter component profiles and meteorological parameters, a generalized linear regression model based on quasi-Poisson distribution was constructed to quantitatively assess the differential contributions of particulate matter components across size ranges to mortality risk.

During the study period, 37 416 circulatory system disease deaths were recorded, with a weekly average mortality of 393.85±93.70 cases. The health effects of particulate matter were most pronounced in the 1.1- 2.1 μm size range, where a 1 interquartile range (IQR) increase was associated with a 6.83% (95%CI: 1.25%-12.72%) elevation in circulatory mortality risk. Component-specific analyses identified chloride ions, potassium, chromium, titanium, and cadmium as significantly correlated with circulatory mortality. Smaller particle sizes exhibited greater toxicity, with heightened sensitivity observed in females and individuals over 65 years old.

Particulate matter health effects demonstrate significant size-dependent characteristics, with diminishing particle sizes correlating to increased toxic complexity. Targeted control strategies are recommended, prioritizing metal components and secondary inorganic ions in particles below 2.5μm.