Objective: Analyze the contribution of common respiratory pathogens to influenza-like illness (ILI) in Beijing during the 2024-2025 surveillance years, and identify the dominant local pathogens and their differences across age groups. Methods: Collect ILI consultation data from secondary-level and above medical institutions in Beijing during the 2024-2025 surveillance year, as well as the positive rates for 22 respiratory pathogens from 19 sentinel hospitals. We used the elastic network model to screen for pathogens, and construct generalized additive models (GAM) for the entire population and each age group separately. Based on the correction of confounding factors such as time and holidays, calculate the attributable contribution of each pathogen to the ILI through the attributable fraction (AF). Results: A total of 1 389 954 cases of ILI were monitored, averaging 26 730 cases per week. A total of 14 pathogens had a positive rate of ≥1.00% among the 22 pathogens. The top 5 pathogens with the highest positive rates were haemophilus influenzae (10.78%,2 188/20 296), streptococcus pneumoniae (10.62%,2 155/20 296), and 2019-nCoV (9.96%,2 021/20 296), influenza virus (9.34%, 1 895/20 296) and rhinovirus (5.79%, 1 175/20 296). In the pathogen attribution analysis, the elastic network model screened out a total of 9 pathogens, with a combined AF of 52.88%, and the number of attributed ILI was 735 008 cases. The top three pathogens by AF (and number of attributed cases) were influenza virus at 20.46% (284 385 cases), rhinovirus at 10.47% (145 528 cases), and human metapneumovirus at 8.09% (112 447 cases). Among the age groups of 0-4 years, 15-59 years and over 60 years, AF of influenza virus was the highest, being 11.71% (95%CI: 0.00%-17.77%), 26.38% (95%CI: 13.60%- 36.94%), and 29.89% (95%CI: 3.30%-37.66%) respectively; among the 5-14 years age group, AF of rhinovirus was the highest, being 9.82% (95%CI: 0.00%-18.37%). Conclusions: During the 2024-2025 surveillance years, the number of patients visiting the outpatient and emergency departments with ILI was caused by multiple pathogens. Influenza virus, rhinovirus, and human metapneumovirus were the main causative pathogens, and their contributions varied across age groups. GAM can effectively measure the disease burden of different pathogens and provide reliable data support for the precise monitoring and stratified prevention and control of respiratory infectious diseases.
Objective: To explore the association between serum testosterone and all-cause mortality in Hainan male centenarians, and the modifying effect of low-density lipoprotein cholesterol (LDL-C). Methods: Based on the China Hainan Centenarian Cohort Study, 167 male participants with complete data were followed up for survival status and death outcomes. Restricted cubic splines (RCS), Cox proportional hazards regression model, and Kaplan-Meier survival curves were used to investigate the relationship between testosterone and mortality. The effect of LDL-C on the relationship between testosterone and all-cause mortality was analyzed using the likelihood ratio test. Results: As of March 31, 2023, the median follow-up time M(Q1, Q3) was 22.90 (10.00, 48.60) months. RCS results showed a linear negative association between testosterone levels and mortality (P=0.042, P for nonlinear=0.061). Kaplan-Meier survival curves analysis indicated that lower testosterone levels were significantly associated with shorter survival time (22.50 months vs. 28.00 months, P=0.006). Interaction analysis revealed that LDL-C influenced the independent predictive ability of testosterone for all-cause mortality in male centenarians (P for linear <0.001; P for nonlinear=0.003). Conclusions: Among male centenarians in Hainan, decreased serum testosterone levels are associated with an increased risk of all-cause mortality. Furthermore, when serum LDL-C levels are low, the association between testosterone levels and higher mortality is more pronounced.
Objective: This study aims to explore the association of lipid indicators, including LDL-C, HDL-C, TC, lipoprotein a (Lpa), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and the composite lipid indicator triglyceride-glucose index (TyG), with the risk of osteoporotic fracture. Methods: A retrospective analysis was performed based on clinical data from 3 987 patients hospitalized in the Department of Osteoporosis at West China Fourth Hospital, Sichuan University, between December 2017 and October 2024. Based on the presence or absence of osteoporotic fracture, the patients were divided into cases and controls. Logistic regression models, mediation effect models, and stratified analysis were applied to explore the association. The area under the curve (AUC) was used to evaluate the model's prediction performance. Maximum likelihood estimation and targeted maximum likelihood estimation were used under the causal contrast weighting framework for causal inference analysis. Results: The level of HDL-C in the case group was higher than that in the control group, while the opposite was true for the levels of ApoA1 and ApoB (all P<0.05). Logistic regression analysis revealed a positive correlation between HDL-C and osteoporotic fracture risk (OR=1.36, 95%CI: 1.12-1.66, P=0.002), while ApoA1 (OR=0.76, 95%CI: 0.64-0.90, P=0.002) and ApoB (OR=0.83, 95%CI: 0.70-0.98, P=0.031) showed a negative correlation with osteoporotic fracture risk. Stratified analysis showed that the effects of HDL-C were mainly observed in elderly patients (≥65 years old), whereas those of ApoA1 and ApoB were mainly observed in elderly and obese patients (BMI≥24.00 kg/m2). Mediation analysis showed that HDL-C indirectly affected fractures through femoral neck and lumbar spine bone density (mediation ratios of 21.10% and 22.50%), while LDL-C was mediated by lumbar spine bone density (mediation ratio of 33.40%). After combining HDL-C, LDL-C, ApoA1, and ApoB indicators, the model's AUC increased significantly (P=0.010), and its predictive performance improved. Causal inference analysis supported that ApoA1 (maximum likelihood estimation: RR=0.83, 95%CI: 0.73-0.92, P=0.002; targeted maximum likelihood estimation: RR=0.81, 95%CI: 0.72-0.94, P<0.001) and ApoB (maximum likelihood estimation: RR=0.84, 95%CI: 0.74-0.94, P=0.004; targeted maximum likelihood estimation: RR=0.85, 95%CI: 0.74-0.96, P=0.009) were inversely associated with osteoporotic fracture risk, the association of HDL-C with osteoporotic fracture was only significant in the maximum likelihood estimation model (RR=1.20, 95%CI: 1.06-1.40, P=0.012). Conclusions: Elevated HDL-C levels were associated with increased risk of osteoporotic fractures, while elevated ApoA1 and ApoB levels were associated with decreased risk, and the association between the latter two was more robust in the causal inference analysis. Lipid indicators are useful for predicting and evaluating osteoporotic fracture risk and have potential clinical value for preventing osteoporotic fracture.
Objective: To explore the relationship between long-term exposure to atmospheric ozone (O3) and adolescent depressive mood, and provide baseline data for the prevention of depression in adolescents and mental health interventions in schools. Methods: Daily environmental pollutant and meteorological data were collected from Inner Mongolia Autonomous Region between 2023 and 2024. A total of 83 601 adolescents from 12 leagues (103 banners/counties/districts) in the region were selected by using stratified random cluster sampling. Depressive symptoms were measured by using the Center for Epidemiologic Studies Depression Scale. A multivariate logistic regression analysis model was used to evaluate the association between O₃ exposure and the prevalence of depression. Results: A total of 83 601 adolescents were surveyed, with a depression detection rate of 18.4% (15 356 individuals). There was a significant association between long-term O₃ exposure and the risk for depression in adolescents. The annual average concentration of O₃ one year prior to the survey showed the greatest effect, with every 10 μg/m³ increase in O₃ exposure associated with a 15% rise in depression risk (OR=1.15, 95%CI:1.10-1.21). Stratified analysis revealed that the following subgroups had significant higher risks for depression:girls (OR=1.36, 95%CI:1.25-1.49), junior high school students (OR=1.39, 95%CI:1.27-1.52), those whose mother's had educational attainment at and above university or college level (OR=1.35, 95%CI:1.15-1.58), and those whose father had educational attainment at or below the primary school level (OR=1.41, 95%CI:1.21-1.65), non-only children (OR=1.28, 95%CI:1.18-1.39), those with parents suffering from chronic diseases (OR=1.26, 95%CI:1.13-1.41), students without personal health conditions (OR=1.27, 95%CI:1.17-1.36), those who had experienced harm (OR=1.42, 95%CI:1.15-1.75), those satisfied with life (OR=1.37, 95%CI:1.20-1.57), participants reporting moderate stress (OR=1.35, 95%CI:1.21-1.50), individuals with daily internet use exceeding 4 hours (OR=1.26, 95%CI:1.06-1.49), members of large families (OR=1.47, 95%CI:1.24-1.74), and residents of the eastern region (OR=1.67, 95%CI:1.50-1.86). The results of the sensitivity analysis model remained relatively stable, indicating that O₃ has an independent effect on the risk of depression onset. Conclusion: There is a statistically significant association between long-term O₃ exposure and the risk of depression in adolescents, which warrants more attention.
Objective: To identify the research priorities on violence against children and health in China. Methods: The Child Health and Nutrition Research Initiative (CHNRI) method was adopted, followed by a standardized process of establishment of research themes and evaluation criteria, collection and scoring of research ideas, and quantitative priority ranking. The research ideas were evaluated by feasibility, effectiveness, potential for paradigm shift, potential for translation and implementation, and equity. Results: A total of 65 experts from 39 universities and research institutions in 23 provinces in China participated in this study. Through two rounds of structured consultation, 91 research ideas on violence against children and health in China were identified. The top three research ideas were investigating the mechanisms linking domestic violence in China to oppositional behavior in children and corresponding intervention models [research priority score (RPS)=0.906, 95%CI: 0.868-0.939; average expert agreement score based on information theory (AEA-e)=0.654, 95%CI: 0.591-0.730], developing family education guidance interventions to reduce children's exposure to violence and evaluating intervention effectiveness (RPS=0.901, 95%CI: 0.862-0.938; AEA-e=0.680, 95%CI: 0.620-0.753), and comparing the prevalence and patterns of violence against children in urban and rural areas (RPS=0.900, 95%CI: 0.861-0.935; AEA-e=0.670, 95%CI: 0.607-0.748). Conclusion: This study established the CHNRI framework of research priorities on violence against children and health in China, which provides a scientific basis for future research directions and resource allocation in this field.
