The relationship between appearance anxiety and depression among students in a medical university in China: a serial multiple mediation model.

Background: Appearance anxiety and depression have become common and global public health problems worldwide, especially among adolescents. However, few studies have revealed the mechanisms between them. This study aimed to explore the multiple mediating roles of interpersonal sensitivity and social support between appearance anxiety and depression among medical college students. Methods: With 13 invalid samples excluded, 724 college students participated in our survey and completed questionnaires. The average age of 724 samples was 19.8 ± 2.02 including freshman to senior year and graduate school and above; 31.9% of the participants were male and 68.1% were female. SPSS 25.0 and Hayes' PROCESS macro were used for statistical description, correlation analysis and built multiple mediation models. Results: Appearance anxiety can not only directly affect depression, but also indirectly affect depression through three significant mediating pathways: (1) IS (B = 0.106, 95% CI [0.082-0.132]), which accounted for 49.77% of the total effect, (2) SS (B = 0.018, 95% CI [0.008-0.031]), which accounted for 8.45% of the total effect, and (3) IS and SS (B = 0.008, 95% CI [0.003-0.014]), which accounted for 3.76% of the total effect. And the total mediating effect was 61.97%. Limitations: It is a cross-sectional research method and the causal relationship is unclear. Conclusions: This study found that lower interpersonal sensitivity and higher social support can effectively reduce depression caused by appearance anxiety among college students. The schools and relevant departments should take measures to reduce the interpersonal sensitivity of college students and establish reliable social support, so as to reduce the occurrence of depression.

Comparison of SARIMA model, Holt-winters model and ETS model in predicting the incidence of foodborne disease.

BACKGROUND: According to the World Health Organization, foodborne disease is a significant public health issue. We will choose the best model to predict foodborne disease by comparison, to provide evidence for government policies to prevent foodborne illness. METHODS: The foodborne disease monthly incidence data from June 2017 to April 2022 were obtained from the Chongqing Nan'an District Center for Disease Prevention and Control. Data from June 2017 to June 2021 were used to train the model, and the last 10 months of incidence were used for prediction and validation The incidence was fitted using the seasonal autoregressive integrated moving average (SARIMA) model, Holt-Winters model and Exponential Smoothing (ETS) model. Besides, we used MSE, MAE, RMSE to determine which model fits better. RESULTS: During June 2017 to April 2022, the incidence of foodborne disease showed seasonal changes, the months with the highest incidence are June to November. The optimal model of SARIMA is SARIMA (1,0,0) (1,1,0)12. The MSE, MAE, RMSE of the Holt-Winters model are 8.78, 2.33 and 2.96 respectively, which less than those of the SARIMA and ETS model, and its prediction curve is closer to the true value. The optimal model has good predictive performance. CONCLUSION: Based on the results, Holt-Winters model produces better prediction accuracy of the model.