Objective To screen the relative risk and protective factors of children patients with asthma after capillary bronchiolitis.Methods The clinical and follow-up data of 220 children patients with capillary bronchiolitis were collected.Sex,treatment method,immunization vaccination,idiosyncrasy,feeding way,family history were investigated and analyzed.The risk factrs of asthma were screened.Results Univariate analysis showed that the incidence of asthma between different genders had no significant difference (P ＞0.05).The asthma incidence in patients with BCG vaccination on time and large-dose immunoglobulin treatment were lower than those in patients without BCG vaccination on time and large-dose immunoglobulin treatnent (P＜ 0.01 ).The asthma incidence in patients with idiosyncrasy,family history was significantly higher than that in patients without idiosyncrasy,family history( P ＜ 0.05 ).The asthma incidence in patients with obesity was higher than that in other children (P ＜ 0.05).The asthma incidence in patients with more than 4 months breast feeding was lower than that in patients with other feeding ways (P ＜ 0.05 ).Multi-factors regression analysis showed that family history,obesity,idiosyncrasy were the risk factors of asthua,and large-dose immunoglobulin treatment,BCG vaccination on time,breast feeding ＞ 4 months were the protective factors.Conclusions Family history,idiosyncrasy,obesity are the risk factors of asthma in children patients with capillary bronchiolitis,and should be focused on olservation.Immunoglobulin treatment and procedural BCG vaccination are the protective factors to reduce the asthma incidence.They are worthy of application especially in children with risk factors.

Objective: The study aims to examine the prospective association and sex differences of pubertal timing and tempo with behavior problems. Methods: Participants from two primary schools in grade 1-3 were selected through clustering convenience sampling method in Bengbu, Anhui Province was established since 2013 (T1), with informed consent, 2 084 students were recruited, and follow-up surveys were conducted in 2015 (T2), 2017 (T3) and 2019 (T4) respectively. Demographic information, non-suicidal self-injury (NSSI), suicidal ideation, suicide plan, suicide attempt and externalizing symptoms were collected through questionnaire investigation, and pubertal development in boys and girls were evaluated by secondary sexual development (testicular volume for boys and breast development for girls). At T4, externalizing symptoms were evaluated by MacArthur Health & Behavior Questionnaire-Child (HBQ-C). Using SAS PROC NLMIXED to fit nonlinear growth model to estimate pubertal timimg and tempo. Puberty timing was classified into early, on-time, and delayed groups; and the puberty tempo into fast, average, and slow groups. Multivariable logistic regression model was used to analyze the predictive effects of modeling pubertal timing and tempo on multi-disciplinary behavior problems in adolescence boys and girls. Results: There were 1 909 people with complete baseline and follow-up questionnaires and puberty development information. Reported rates of externalizing symptoms, NSSI, suicidal ideation, suicide plan, suicide attempt and were 13.9% (265), 24.4% (466), 14.2% (271), 4.6% (88) and 2.3% (43) respectively. Logistic regression analysis results show that fast pubertal tempo increases the risk of externalization [OR(95%CI)=2.85(1.53-5.31) and NSSI [OR(95%CI)=2.11(1.22-3.65)] for boys, and the risk of suicide attempt [OR(95%CI)=2.99(1.07-8.35)] for girls. Slow tempo can reduce the risk of suicidal ideation and suicide plan for boys [OR(95%CI)=0.40(0.18-0.87), 0.07(0.01-0.67)], the externalizing symptoms and suicidal ideation for girls [OR(95%CI)=0.33(0.13-0.81)，0.38(0.17-0.85)]. Early pubertal timing will increase the risk of suicide plan for boys [OR(95%CI)=3.60(1.04-12.43)], meanwhile late timing can reduce the risk of NSSI in girls [OR(95%CI)=0.39(0.19-0.80)]. Conclusion The tempo of pubertal development may be a useful predictor of adolescent behavioral problems.

Objective: To explore the correlation between screen time, exposure time to different screens and psychology behaviors of preschool children. Methods: A total of 2 582 children from kindergartens in urban Xuzhou areas were recruited to perform the physical examination, a cluster sampling method being explored. Parent questionnaires were performed to understand the time of screens and children’s psychology behaviors. Multi-linear regression and Logistic regression models were also used to analyze the correlation between them in preschool children. Results: The prevalence of abnormal internalization behavior of preschool children in Xuzhou City was 3.8%, the detection rate of abnormal externalization behavior was 22.4%, and the detection rate of prosocial behavior abnormality was 20.9%. The time spent by the preschool boys on TV time, learning day screen time and one-week video time is significantly higher than the girls (P<0.05). After adjusting for age and gender, the results of multiple linear regression analysis showed that the longer the average screen time, the more serious the problem of internalizing and externalizing problems; and the longer the average screen time of the weekend and the week, the worse the prosocial behavior of children (P<0.05). After correcting multiple covariates, it was found that the average screen time was positively correlated with children’s internal and external behavioral problems (P<0.05). Logistic regression analysis showed that after adjusting for age and gender, the average screen time of study day, weekend and week was a risk factor for preschool children’s internal and external behavior problems, and the average weekly screen time was a protective factor for prosocial behavior (P<0.05). After adjusting for multiple covariates, the learning day and the average weekly screen time were risk factors for children’s internal and external behavior (P<0.05). In addition, the results of association analysis between different types of video time exposure and psychological behavior showed that after adjusting for age and gender, all types of video exposures affected the internal and external behaviors of patients (P<0.05); after correcting multiple covariates The association was still statistically significant (P<0.05). Logistic regression analysis showed that after adjusting for age and gender, the exposure time of each type of video screen was a risk factor for children’s internal and external behavior problems (P<0.05). After correcting multiple covariates, all types of video exposure were internalized behavior problems. The risk factors, and the video time of other electronic products were risk factors for externalization behavior problems (P<0.05). Conclusion Average screen time has a significant positive correlation with psychological behavior, and the exposure time of screens such as TV and mobile phone could increase the incidence of psychological behaviors in preschool children.

Objective: To investigate the correlation of lifestyle with obesity among migrant children in Shanghai and to provide reference for preventing and controlling obesity of the migrant children. Methods: Stratified cluster sampling was adopted to conduct a routine physical examination and investigate lifestyle among 3 894 students (7-13 years old) in June 2018. Association of lifestyle with obesity was analyzed. Chi-square test was used to compare differences in obesity prevalence among migrant children with different lifestyles. Logistic regression was used to explore association with lifestyle and obesity. Results: Chi-square test showed that obesity rate in males (18.6%) was significantly higher than that of females(10.7%)(χ2=24.79, P<0.01). Sleep duration, screen time, exercise frequency correlated with obesity(χ2=35.13, 9.29, 7.98, P<0.05). Regression results illustrated that insufficient sleep duration associated higher risk of obesity, the odds ratio for obesity was (OR=2.61, 95%CI=1.64-4.11) and (OR=1.54, 95%CI=1.27-1.86) among children with sleep duration less than 6 h/d and 6-8 h/d, compared with children with more than 8 h/d sleep time, after multiple covariates controlled. Conclusion Sleep deprivation positively associated with obesity, ensuring adequate sleep time might help prevent obesity among migrant children.

Objective:To examine the prospective association of pubertal timing and tempo with depressive symptoms in adolescents.Methods:Since 2013, 2 084 students in grade 1-3 were selected from two primary schools in Bengbu, Anhui Province were selected by using convenience sampling method to establish the adolescence pubertal development cohort. Followed up for 6 years, physical examination, secondary sexual development evaluation (testicular volume for boys and breast development for girls) and depressive symptoms were evaluated biennially. Non-linear growth model was used to estimate pubertal timing and tempo for boys and girls respectively. Depressive symptoms were interviewed by using the Short Mood & Feeling Questionnaire (SMFQ) at baseline and Mood & Feeling Questionnaire (MFQ) during follow-up for students in grade 1-2. Children Depression Inventory (CDI) was used for students in grade 3 at baseline and during follow-up. Depressive symptom scores were standardized by using the Z-score method. Multivariate linear regression model was used to analyze the predictive effects of modeling pubertal timing and tempo on depressive symptoms of adolescence boys and girls. Results:There were 1 909 students with complete questionnaire and puberty development information, including 1 052 boys (59.19%) and 857 girls (43.81%), with average age about (13.94±0.87) years and 91.60 percent follow-up rate. The average modeling pubertal timing of girls (11.25 years) was earlier than that of boys (12.70 years), and the average pubertal tempo of girls about 1.47 Tanner stage/year was faster than that of boys about 1.28 Tanner stage/year. After controlling for depressive symptoms, maternal education and adverse childhood experiences at baseline and age, body mass index (BMI) classification and sleep time during follow-up, this predictive effect of pubertal timing and tempo on depressive symptoms was only significant among girls. Compared with girls with on time pubertal timing, girls in the delay timing group had a lower level of depressive symptoms (β=-0.19, 95% CI:-0.34,-0.01). Compared with girls in average pubertal tempo group, the fast tempo group associated with an increasing risk of depressive symptoms (β =0.23, 95% CI: 0.05, 0.40), while the slow tempo group associated with an decreasing risk of depressive symptoms (β =-0.21, 95％ CI:-0.39,-0.03). Insignificant effects were found in puberty timing and tempo on depressive symptoms of boys ( P>0.05). Conclusion:Fast pubertal tempo increases the risk of development of depressive symptoms of adolescent girls. There is no predictive effect of pubertal timing and tempo on depression symptoms of adolescent boys.

Objective:To examine the prospective association of pubertal timing and tempo with depressive symptoms in adolescents.Methods:Since 2013, 2 084 students in grade 1-3 were selected from two primary schools in Bengbu, Anhui Province were selected by using convenience sampling method to establish the adolescence pubertal development cohort. Followed up for 6 years, physical examination, secondary sexual development evaluation (testicular volume for boys and breast development for girls) and depressive symptoms were evaluated biennially. Non-linear growth model was used to estimate pubertal timing and tempo for boys and girls respectively. Depressive symptoms were interviewed by using the Short Mood & Feeling Questionnaire (SMFQ) at baseline and Mood & Feeling Questionnaire (MFQ) during follow-up for students in grade 1-2. Children Depression Inventory (CDI) was used for students in grade 3 at baseline and during follow-up. Depressive symptom scores were standardized by using the Z-score method. Multivariate linear regression model was used to analyze the predictive effects of modeling pubertal timing and tempo on depressive symptoms of adolescence boys and girls. Results:There were 1 909 students with complete questionnaire and puberty development information, including 1 052 boys (59.19%) and 857 girls (43.81%), with average age about (13.94±0.87) years and 91.60 percent follow-up rate. The average modeling pubertal timing of girls (11.25 years) was earlier than that of boys (12.70 years), and the average pubertal tempo of girls about 1.47 Tanner stage/year was faster than that of boys about 1.28 Tanner stage/year. After controlling for depressive symptoms, maternal education and adverse childhood experiences at baseline and age, body mass index (BMI) classification and sleep time during follow-up, this predictive effect of pubertal timing and tempo on depressive symptoms was only significant among girls. Compared with girls with on time pubertal timing, girls in the delay timing group had a lower level of depressive symptoms (β=-0.19, 95% CI:-0.34,-0.01). Compared with girls in average pubertal tempo group, the fast tempo group associated with an increasing risk of depressive symptoms (β =0.23, 95% CI: 0.05, 0.40), while the slow tempo group associated with an decreasing risk of depressive symptoms (β =-0.21, 95％ CI:-0.39,-0.03). Insignificant effects were found in puberty timing and tempo on depressive symptoms of boys ( P>0.05). Conclusion:Fast pubertal tempo increases the risk of development of depressive symptoms of adolescent girls. There is no predictive effect of pubertal timing and tempo on depression symptoms of adolescent boys.

Objective:To explore the protective effects of breastfeeding on behavioral problems at 4 years in children born to mothers with gestational diabetes mellitus (GDM).Methods:Based on the Ma' anshan Birth Cohort (MABC) study, 305 GDM women and their children were recruited in this study from Ma' anshan Maternal and Child Health Care Hospital from May 2013 to September 2014. Total breastfeeding duration was followed up at 42 d, 3, 6, 12, and 18 months postpartum as well as the breastfeeding intensity within 6 months. All the subjects were divided into breastfeeding group ( n=256, including exclusive breastfeeding and mixed feeding) or bottle feeding group ( n=49). Internalizing and externalizing behavioral problems at age 4 were assessed using Achenbach Child Behavior Checklist (CBCL/1.5~5) and their association with breastfeeding were analyzed using robust Poisson regression. Controlling false discovery rate was applied for multiple test correction. Results:Compared with bottle feeding, breastfeeding was a protective factor for depression in children ( RR=0.23, 95% CI: 0.05-0.98, q=0.048) when the duration was 4-5 months; for somatic complaints ( RR=0.36, 95% CI: 0.14-0.95, q=0.047) and anxiety ( RR=0.19, 95% CI: 0.06-0.62, q=0.010) with a breastfeeding duration of 6-11 months; and for depression ( RR=0.46, 95% CI: 0.25-0.83, q=0.039) and anxiety ( RR=0.12, 95% CI: 0.03-0.49, q=0.006) with a breastfeeding duration of 12 months and above. Compared with bottle feeding within 6 months, mixed feeding had a protective effect on somatic complaints ( RR=0.29, 95% CI: 0.13-0.64, q=0.026) and anxiety ( RR=0.18, 95% CI:0.07-0.52, q=0.002). Conclusions:The findings suggested that breastfeeding had a protective effect on behavioral problems at age 4 in children exposed to GDM. Women with GDM should be encouraged to breastfeed.

Sleep regulates many body processes, including neuroendocrine immunity. There is increasing evidence that sleep can affect glucose metabolism. Pregnant women are more prone to sleep problems than the general population, and therefore the relationship between sleep during pregnancy and gestational diabetes mellitus is gradually under attention. This paper reviews the associations between sleep during pregnancy and gestational diabetes mellitus in terms of sleep quality, sleep duration, sleep rhythm, and possible biological mechanisms to provide references for preventing and controlling gestational diabetes mellitus.

Objective To understand the epidemiological characteristics of the cases of COVID-19 epidemic clusters, and explore the influence of family factors and social factors such as group activities on the spread of the disease. Methods The data of cases of COVID-19 epidemic clusters from 19 January, 2020 to 25 February, 2020 were collected from the official platforms of 36 cities in 6 provinces in China. Descriptive statistical methods, χ 2 test and curve fitting were used to analyze the epidemiological characteristics of the clustered cases. Results By 25 February, 2020, the data of 1 052 cases in 366 epidemic clusters were collected. In these clustered cases, 86.9%(914/1 050) occurred in families. Among the 1 046 cases with gender information, 513 were males (49.0%) and 533 were females (51.0%). The cases were mainly young adults between 18 and 59 years old, accounting for 68.5% (711/1 038). In the 366 epidemic clusters , the clusters in which the first confirmed cases with the history of sojourn in Wuhan or Hubei accounted for 47.0%(172/366). From 19 January to 3 February, 2020, the first confirmed cases with Wuhan or Hubei sojourn history accounted for 66.5%. From 4 to 25 February, the first confirmed cases who had Wuhan or Hubei sojourn history accounted for only 18.2%. The median of interval between the first generation case onset and the second generation case onset was 5 (2-8) days. The median of onset- diagnosis interval of the initial cases was 6 (3-9) days, and the median of onset-diagnosis interval of the secondary cases was 5 (3-8) days. Conclusions Epidemic clusters of COVID-19 were common in many cities outside Wuhan and Hubei. Close contact in family was one of the main causes for the spread of household transmission of the virus. After 4 February, the epidemic clusters were mainly caused by the first generation or second generation cases in local areas, and the time for diagnosis became shorter.

We used the epidemic data of COVID-19 published on the official website of the municipal health commission in Anhui province. We mapped the spatiotemporal changes of confirmed cases, fitted the epidemic situation by the population growth curve at different stages and took statistical description and analysis of the epidemic situation in Anhui province. It was found that the cumulative incidence of COVID-19 was 156/100 000 by February 18, 2020 and the trend of COVID-19 epidemic declined after February 7, changing from J curve to S curve. The actual number of new cases began to decrease from February 2 to February 4 due to the time of case report and actual onset delayed by 3 to 5 days.