Mediating effect of physical sub-health in the association of sugar-sweetened beverages consumption with depressive symptoms in Chinese college students: A structural equation model.

BACKGROUND: Although previous findings with small samples indicated that sugar-sweetened beverages (SSB) consumption was associated with depressive symptoms in adolescents, the mediating effect of physical sub-health in the association is unknown. METHODS: A survey was conducted among freshmen from 11 provinces in China. A food frequency questionnaire was used to collect dietary behavior, and patient health questionnaire-9 items was used to assess depressive symptoms. A generalized linear model was used to analyze the association between SSB consumption, physical sub-health and depressive symptoms. The structural equation model was used to analyze the mediating effect of physical sub-health. RESULTS: Of the 31,856 participants, 36.5 % had positive depressive symptoms. After adjusting for variables, carbonate beverages (ß = 0.11; 95%CI: 0.07-0.15; P = 0.000) and milk tea (ß = 0.07; 95%CI: 0.01-0.13; P = 0.021) consumption was associated with depressive symptoms in boys. Carbonate beverages (ß = 0.09; 95%CI: 0.05-0.13; P = 0.000), tea beverages (ß = 0.09; 95%CI: 0.04-0.13; P = 0.000), and milk tea (ß = 0.08; 95%CI: 0.04-0.11; P = 0.000) consumption was associated with depressive symptoms in girls. The mediating effect of physical sub-health accounted for 81.3 % of the total effect in the mediating model of SSB associated with depressive symptoms. LIMITATIONS: Retrospective survey has certain information bias. Association observed in the cross-sectional study is uncertain. CONCLUSIONS: The present study suggests that the consumption of SSB associated with depressive symptoms in Chinese college students, and physical sub-health plays a complete mediating role in the association.

A decision tree model of hypertension among college students in Yunnan Province, China.

PURPOSE: Previous studies have indicated that the prevalence rate of hypertension in adolescents is high, but it has not received much attention and the influencing factors are unclear, especially in Yunnan Province, China. MATERIALS AND METHODS: A cluster sampling method was used to investigate 4781 freshmen in a college in Kunming, Yunnan Province from November to December. Demographic and lifestyle data were collected using questionnaires, and height, weight and blood pressure were measured. Decision tree model of hypertension in college students was established by Chi-square automatic interactive detection method. RESULTS: Prevalence of prehypertension of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were detected in 33.9% and 32.1%, respectively. Prevalence of hypertension of SBP and DBP was detected in 1.2% and 7.2%, respectively. The hypertension and prehypertension decision tree of SBP has gender (χ2 = 728.64, p < .001) at the first level and body mass index (BMI) (boys: χ2 = 55.98, p < .001; girls: χ2 = 79.58, p < .001) at the second level. The hypertension and prehypertension decision tree of DBP has gender (χ2 = 381.83, p < .001) at the first level, BMI (boys: χ2 = 40.54, p < .001; girls: χ2 = 48.79, p < .001) at the second level, only children (χ2 = 6.43, p = .04) and red wine consumption (χ2 = 8.17, p = .017) at the third level. CONCLUSIONS: The present study suggests that gender, BMI, only children and red wine consumption were the main factors affecting hypertension in college students in southwest border areas of China.

Hypertension in Chinese adolescent is generally ignored. This study first reports the prevalence of hypertension in adolescents in Yunnan Province, China.Four thousand seven hundred and eighty-one freshmen were surveyed and height, weight and blood pressure were measured. A decision tree model was used to analyze the predictors of hypertension.The study demonstrated that gender, BMI, only children and red wine predict hypertension in adolescents.

Mobile phone use addiction, insomnia, and depressive symptoms in adolescents from ethnic minority areas in China: A latent variable mediation model.

BACKGROUND AND AIMS: Although previous studies have discussed the association between mobile phone use addiction (MPUA) and depressive symptoms in adolescents, studies on Chinese adolescents are limited, with no studies being reported on adolescents from ethnic minority areas within Yunnan. This study aimed to explore the mediating mechanism of insomnia in the association between MPUA and depressive symptoms among Yunnan college students. METHODS: A sample survey was conducted among 10,121 college students from four colleges in Yunnan province, China, from November to December 2021 via cluster sampling. Insomnia was assessed using the Insomnia Severity Index Scale, MPUA was assessed using the Self-Rating Questionnaire for Adolescent Problematic Mobile Phone Use, and depressive symptoms were assessed using the Patient Health Questionnaire-9 items. A generalized linear model was used to analyze the association between MPUA, insomnia, and depressive symptoms. The structural equation model was used to analyze the mediating effect of insomnia. RESULTS: The positive rates of depressive symptoms, MPUA, and insomnia were 39.2 %, 30.8 %, and 22.3 %, respectively. After adjusting for the demographic and confounding variables, MPUA (ß = 0.14, 95 % CI: 0.13-0.15, P < 0.01) and insomnia (ß = 0.44, 95 % CI: 0.43-0.46, P < 0.01) were associated with depressive symptoms (P < 0.05). The mediating effect of insomnia accounted for 34.7 % of the total effect in the mediating model of MPUA associated with depressive symptoms. CONCLUSION: Our findings suggest that insomnia partially mediates the association between MPUA and depressive symptoms among Yunnan college students.

Basic reproduction number of 2019 Novel Coronavirus Disease in Major Endemic Areas of China: A latent profile analysis

ObjectiveThe aim of the study is to analyze the latent class of basic reproduction number (R0) trend of 2019 novel coronavirus disease (COVID-19) in major endemic areas of China. MethodsThe provinces that reported more than 500 cases of COVID-19 till February 18, 2020 were selected as the major endemic area. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R0 of COVID-19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID-19, respectively. The latent class of R0 was analyzed using a latent profile analysis model. ResultsThe median R0 calculated from SARS and COVID-19 parameters were 1.84 - 3.18 and 1.74 - 2.91, respectively. The R0 calculated from the SARS parameters was greater than that of calculated from the COVID-19 parameters (Z = -4.782 - -4.623, P < 0.01). Both R0 can be divided into three latent classes. The initial value of R0 in class 1 (Shandong Province, Sichuan Province and Chongqing Municipality) was relatively low and decreases slowly. The initial value of R0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province and Jiangsu Province) was relatively high and decreases rapidly. Moreover, the initial value of R0 of class 3 (Hubei Province) was between that of class 1 and class 2, but the higher level of R0 lasts longer and decreases slowly. ConclusionThe results indicated that overall trend of R0 has been falling with the strengthening of Chinas comprehensive prevention and control measures for COVID-19, however, presents regional differences.

Analysis on epidemic situation and spatiotemporal changes of COVID-19 in Anhui / 中华预防医学杂志

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.