Objective: To apply network analysis for exploring the relationship of maltreatment experiences and peer relationships with school bullying among middle school students, so as to provide empirical evidences for the development of targeted intervention programs. Methods: From March to April 2024, a total of 2 119 middle school students aged 12-18 in Shantou City were selected through stratified cluster random sampling. Self administered questionnaire was used to collect data on bullying experiences, maltreatment, and peer relationships. The Glasso network model was employed to estimate network structure. Results: The strongest edge in the network of maltreatment experiences, peer relationships and school bullying was the connecting line connecting peer acceptance and peer terrorized low self esteem (edge weight=0.59) among middle school students. The network faked fraudulent victimization was the most central node in the whole network (strength=7.98). The bridge symptoms of the network were sexual abuse, property bullying of others, relational bullying victimization, and verbal bullying of others, with the strongest bridge node being sexual abuse (bridge strength=1.07). In the accuracy estimation of centrality indices, closeness centrality demonstrated the highest accuracy, followed by strength and betweenness, with coefficient of stability of 0.60, 0.44 and 0.21, respectively. The stability of the network was good. Conclusion Peer acceptance has the strongest correlation with peer fear and inferiority, and is closely related to emotional abuse and emotional neglect.

This study aimed to investigate the association between secondhand smoke exposure in different places and overweight/obesity among children and adolescents. Children and adolescents aged 7 to 18 years old in Hunan Province were recruited for questionnaire surveys and physical examinations using a multi-stage stratified cluster sampling method. Secondhand smoke exposure was evaluated according to the answer to the question, "Has someone smoked in front of you in the last 7 days?". Overweight and obesity were determined using BMI-for-age and BMI-for-sex according to the national standard WS/T586-2018. Multivariate logistic regression was used to explore the association between secondhand smoke exposure in different places and overweight/obesity among children and adolescents. A total of 187 863 participants were included in this study, with a prevalence of overweight and obesity of 28.4%. The prevalence of secondhand smoke exposure at home, school and other public places was 25.5%, 12.6% and 32.3%, respectively. Children and adolescents with secondhand smoke exposure at home, school and other public places had a higher prevalence of overweight and obesity than those without exposure. After adjusting for confounding factors, secondhand smoke exposure was positively associated with overweight and obesity among children and adolescents, and exposure at home showed the closest association ( OR=1.091; 95% CI: 1.066-1.117). In conclusion, secondhand smoke exposure, especially at home, significantly increases the risk of overweight and obesity among children and adolescents. Comprehensive strategies should be implemented to avoid secondhand smoke exposure and protect children and adolescents from overweight and obesity.

Objective:To determine the detection rate of myopia among primary and secondary school students in Hunan Province in 2022 and to analyze the influencing factors at both the school and individual levels, thereby providing a scientific basis for developing myopia prevention and control strategies.Methods:From October to November 2022, a multi-stage stratified cluster random sampling method was employed to select students from Year 4 of primary school to Year 3 of senior high school across 14 prefecture-level (autonomous prefecture) cities in Hunan Province for vision screening and questionnaire surveys. A multilevel regression model was utilized to analyze the influencing factors of myopia at both the school and individual levels.Results:A total of 189 343 primary and secondary school students were included in this study. The overall myopia detection rate was 55.56%, with a significantly higher prevalence observed in female students (60.49%) compared to males (51.03%) and in urban students (59.12%) versus those from rural areas (53.50%). A marked upward trend in myopia prevalence was identified with advancing grade levels (trend test χ2=16 246.13, P<0.001). Multilevel regression analysis revealed that at the individual level, female gender, higher grade level, parental myopia history, daily homework duration ≥2 hours after school, improper reading/writing postures, and taking breaks only after more than 15 minutes of near work were associated with an increased risk of myopia. Conversely, adequate sleep duration, outdoor activity ≥2 hours, and outdoor breaks during recess demonstrated protective effects. At the school level, non-compliant blackboard illumination uniformity emerged as a significant risk factor for myopia development. Conclusions:The detection rate of myopia among primary and secondary school students in Hunan Province remains relatively high and is associated with multiple factors at both the school and individual levels. Targeted interventions should be implemented at different levels to mitigate the risk of myopia.

OBJECTIVES: The prevalence of overweight and obesity among children and adolescents continues to rise, becoming one of the most serious global public health issues of the 21st century. Given the differing growth and development environments between urban and rural children, associated risk factors also vary. This study aims to explore the distribution characteristics and influencing factors of overweight and obesity among urban and rural primary and secondary school students in Hunan Province, providing scientific evidence for targeted interventions. METHODS: A stratified, randomized cluster sampling method was used to select participants. A total of 197 084 students from primary and secondary schools across 14 prefectures in Hunan Province underwent physical examinations and questionnaire surveys. Population and spatial distribution characteristics of overweight and obesity were analyzed. Spatial distribution maps and spatial autocorrelation analyses were conducted using ArcGIS. Multivariate Logistic regression was used to identify influencing factors for overweight and obesity. RESULTS: The overall overweight and obesity rates among students in Hunan Province were 14.7% and 10.9%, respectively. Both rates were higher in urban areas than in rural counties (16.0% vs 13.9% for overweight; 12.1% vs 10.2% for obesity). Among both urban and rural students, boys had higher rates of overweight and obesity than girls. Higher-grade students had a higher overweight rate but a lower obesity rate than lower-grade students. In urban areas, the overweight and obesity rates of Han Chinese primary and secondary school students are lower than those of ethnic minority students (both P<0.05). In rural areas, the obesity rate of Han primary and secondary school students is lower than that of ethnic students (P<0.05). Across cities and prefectures, urban overweight and obesity rates ranged from 14.7% to 18.7% and 8.4% to 20.6% respectively, while rural rates ranged from 10.9% to 17.2% and 6.6% to 13.7% respectively. Spatial autocorrelation analysis revealed high-value clusters of overweight/obesity in urban areas of Changde and Zhangjiajie, and in rural areas of Loudi, Huaihua, and Shaoyang. Multivariate Logistic regression showed that gender, school stage, ethnicity, frequency of fresh vegetable intake, and sleep duration were associated with overweight and/or obesity in both urban and rural students. In urban students, frequency of fried food and fresh fruit intake, breakfast habits, physical activity on weekdays and holidays, and screen time on computers were also significant. In rural students, TV viewing time and sedentary duration were additional relevant factors. CONCLUSIONS The situation of overweight and obesity among primary and secondary school students in Hunan Province remains concerning. Greater attention should be paid to regions with high-value clusters of overweight/obesity, and targeted interventions should be developed based on urban-rural differences in influencing factors.
Humans ; China/epidemiology* ; Adolescent ; Male ; Female ; Rural Population/statistics & numerical data* ; Child ; Overweight/epidemiology* ; Students/statistics & numerical data* ; Urban Population/statistics & numerical data* ; Risk Factors ; Prevalence ; Obesity/epidemiology* ; Surveys and Questionnaires ; Pediatric Obesity/epidemiology* ; Schools

OBJECTIVES: The detection rate of scoliosis among school-aged children has been rising annually, varying by region, and has become a major public health concern affecting both physical and mental health. Its onset is multifactorial, and early screening combined with targeted interventions can alter disease progression. This study aims to investigate the prevalence and influencing factors of scoliosis among primary and secondary school students in Hunan Province, providing scientific evidence for targeted prevention strategies. METHODS: A stratified, randomized cluster sampling method was used to select 281 401 students from 14 prefecture-level cities in Hunan Province for scoliosis screening, physical examination, and questionnaire survey. The chi-square test was used for group comparisons, and trend chi-square test analyzed differences in screening positive rate by age. A multilevel regression model was applied to identify influencing factors, and ArcGIS was used to visualize spatial distribution patterns of scoliosis. RESULTS: The overall screening positive rate for scoliosis among Hunan students was 1.61%. Urban areas had a significantly higher rate than rural counties (2.81% vs 0.98%; P<0.01). The rate was equal between boys and girls (1.61% each). Underweight students had a higher rate than those with normal weight, overweight, or obesity (P<0.01). Stratified by age, urban students aged 6-18 years consistently showed higher positive rates than rural peers (P<0.001). No significant gender differences were observed at most ages (all P>0.05), except at age 11, where the females had a higher rate (1.28% vs 1.02%; P=0.048). After age 11, underweight students exhibited significantly higher positive rates than those with normal or higher BMI(all P<0.05). Across all groups, urban/rural, male/female, underweight/normal/overweight/obese, the scoliosis rate increased with age. By region, the screening positive rate ranged from 0.38% to 3.36%, with the top three being Chenzhou (3.36%), Xiangtan (2.78%), and Hengyang (2.71%), while the lowest was Xiangxi Tujia and Miao Autonomous Prefecture (0.38%). Multilevel regression analysis revealed that age (OR=1.160, 95% CI 1.135 to 1.186) and urban residence (OR=2.497, 95% CI 1.946 to 3.205) were positively associated with scoliosis risk (both P<0.01). Conversely, female gender (OR=0.931, 95% CI 0.874 to 0.993), normal nutritional status (OR=0.751, 95% CI 0.671 to 0.840), overweight (OR=0.513, 95% CI 0.447 to 0.590), obesity (OR=0.418, 95% CI 0.358 to 0.489), and engaging in ≥ 60 minutes of moderate-to-vigorous physical activity 2 to 4 days (OR=0.928, 95% CI 0.865 to 0.996) or 5 to 7 days per week (OR=0.912, 95% CI 0.833 to 0.998) were negatively associated with scoliosis risk (all P<0.05). CONCLUSIONS The prevalence of scoliosis among primary and secondary school students in Hunan Province is relatively high and is significantly associated with age, gender, urban-rural status, nutritional condition, and physical activity frequency. Targeted interventions and enhanced monitoring in high-risk regions and populations are essential to prevent and control scoliosis.
Humans ; Scoliosis/epidemiology* ; Male ; Female ; Adolescent ; China/epidemiology* ; Prevalence ; Child ; Students/statistics & numerical data* ; Rural Population/statistics & numerical data* ; Urban Population/statistics & numerical data* ; Surveys and Questionnaires ; Risk Factors ; Thinness/epidemiology*

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective:To determine the detection rate of myopia among primary and secondary school students in Hunan Province in 2022 and to analyze the influencing factors at both the school and individual levels, thereby providing a scientific basis for developing myopia prevention and control strategies.Methods:From October to November 2022, a multi-stage stratified cluster random sampling method was employed to select students from Year 4 of primary school to Year 3 of senior high school across 14 prefecture-level (autonomous prefecture) cities in Hunan Province for vision screening and questionnaire surveys. A multilevel regression model was utilized to analyze the influencing factors of myopia at both the school and individual levels.Results:A total of 189 343 primary and secondary school students were included in this study. The overall myopia detection rate was 55.56%, with a significantly higher prevalence observed in female students (60.49%) compared to males (51.03%) and in urban students (59.12%) versus those from rural areas (53.50%). A marked upward trend in myopia prevalence was identified with advancing grade levels (trend test χ2=16 246.13, P<0.001). Multilevel regression analysis revealed that at the individual level, female gender, higher grade level, parental myopia history, daily homework duration ≥2 hours after school, improper reading/writing postures, and taking breaks only after more than 15 minutes of near work were associated with an increased risk of myopia. Conversely, adequate sleep duration, outdoor activity ≥2 hours, and outdoor breaks during recess demonstrated protective effects. At the school level, non-compliant blackboard illumination uniformity emerged as a significant risk factor for myopia development. Conclusions:The detection rate of myopia among primary and secondary school students in Hunan Province remains relatively high and is associated with multiple factors at both the school and individual levels. Targeted interventions should be implemented at different levels to mitigate the risk of myopia.

This study aimed to investigate the association between secondhand smoke exposure in different places and overweight/obesity among children and adolescents. Children and adolescents aged 7 to 18 years old in Hunan Province were recruited for questionnaire surveys and physical examinations using a multi-stage stratified cluster sampling method. Secondhand smoke exposure was evaluated according to the answer to the question, "Has someone smoked in front of you in the last 7 days?". Overweight and obesity were determined using BMI-for-age and BMI-for-sex according to the national standard WS/T586-2018. Multivariate logistic regression was used to explore the association between secondhand smoke exposure in different places and overweight/obesity among children and adolescents. A total of 187 863 participants were included in this study, with a prevalence of overweight and obesity of 28.4%. The prevalence of secondhand smoke exposure at home, school and other public places was 25.5%, 12.6% and 32.3%, respectively. Children and adolescents with secondhand smoke exposure at home, school and other public places had a higher prevalence of overweight and obesity than those without exposure. After adjusting for confounding factors, secondhand smoke exposure was positively associated with overweight and obesity among children and adolescents, and exposure at home showed the closest association ( OR=1.091; 95% CI: 1.066-1.117). In conclusion, secondhand smoke exposure, especially at home, significantly increases the risk of overweight and obesity among children and adolescents. Comprehensive strategies should be implemented to avoid secondhand smoke exposure and protect children and adolescents from overweight and obesity.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective: To investigate the prevalence of anemia among students aged 6-15 years old participated in the Nutrition Improvement Program for Rural Compulsory Education Program in 2012-2021 in Hunan Province, and to explore associated factors. Methods: A cluster random sampling method was used. The survey was conducted among compulsory education students who came from counties in Hunan Province where nutrition improvement plan and key surveillance were carried out. Height and weight were measured, hemoglobin, dietary habits and health knowledge were collected. Results: Since the implementation of nutrition improvement plan in 2012, the anemia rate decreased from 12.8% to 9.3%, the average hemoglobin content decreased from 133.9 g/L to 130.0 g/L. The proportion of students eating meat every day increased from 20.4% to 47.3%. Students eating three or more servings of table tennis ball size of meal increased from 21.4% to 47.3%. The proportion of students eating more than three kinds of vegetables per day was 39.1% in 2016 and 32.6% in 2019, and less than 30% in other years. The proportion of students eating three or more servings of table tennis ball size of vegetables increased from 19.5% to 39.6%, and the proportion of students eating snack less than once a day increased from 13.1% to 76.9%. The proportion of students drinking beverages less than once a day increased from 36.5% to 90.7%. Girls, older than 12 years, not eating meat every day, and who eat less than 1 serving of table tennis ball size of meat, eat less than 3 servings of table tennis ball size of vegetables and fruit at one time, and who eat less than 3 kinds of vegetables every day, and those who grow slowly had higher risk of anemia. Potential factors of anemia in students varied by survey year. Conclusion The implementation of the compulsory education has improved the nutritional status of compulsory education students in rural areas of Hunan Province, but anemia still occurs. Potential factors associated with childhood anemia are changing. Child health literacy education should adapt to the changing social and economic enviornment, as well as lifestyles. Targeted and appropriate nutritional education and intervention are greatly needed.