ABSTRACT
Objective@#To analyze epidemiological characteristics of campus bullying among primary and middle school students in central China to explore its relation with mental health problems, and to provide a reference for the campus bullying prevention.@*Methods@#Stratified cluster sampling method was used to select primary and middle school 10 581 students from Anyang, Nanyang and Xinxiang cities of Henan Province, Middle School Students Mental Health Scale and the Self designed Scale of Adolescent Bullying Behavior were used to analyze the relationship between mental health problems with campus bullying behavior.@*Results@#The total report rate of bullying penetrator was 12.5% among students in the three cities. Among primary and middle school students with mental health problems such as hostility, interpersonal stress, academic pressure and emotional imbalance, the detection rate of bullying behavior was 24.2%, 20.3%, 19.4% and 20.1%, respectively. The results of multivariate analysis showed that hostility symptoms ( OR =3.78, 95% CI =1.71-8.32), interpersonal stress ( OR =3.50, 95% CI = 1.62 -7.57), academic pressure ( OR = 1.62 , 95% CI =1.21-2.16) and emotional imbalance ( OR =2.80, 95% CI =1.41-5.56) showed a significant impact on campus bullying ( P <0.05).@*Conclusion@#Mental health problems of primary and middle school students are closely related to the occurrence of bullying behavior. It is necessary to pay attention to the mental health education of bullies and intervene bullying behaviors from the source.
ABSTRACT
Background@#The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM. @*Methods@#A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools. @*Results@#Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ2=3.2756, P=0.9159), LBM1 (χ2=7.749, P=0.4584), LBM2 (χ2=10.023, P=0.2634), and simplified model (χ2=12.294, P=0.1387). @*Conclusion@#LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.
ABSTRACT
Background@#The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM. @*Methods@#A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools. @*Results@#Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ2=3.2756, P=0.9159), LBM1 (χ2=7.749, P=0.4584), LBM2 (χ2=10.023, P=0.2634), and simplified model (χ2=12.294, P=0.1387). @*Conclusion@#LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.