Although the emphasis on behaviors of hand-washing and mask-wearing was repeated during the pandemic of Coronavirus Disease 2019 (COVID-19), not everyone paid enough attention to this. A descriptive statistic was used to make sense of the status of hand hygiene and mask-wearing among primary school students in Wuhan, China. A binary logistic regression analysis was conducted to identify the risk factors affecting the behaviors of hand-washing and mask-wearing. p < 0.05 (two-sides) was considered as significant at statistics. 42.05% of the primary school students showed a good behavior of hand-washing, while 51.60% had a good behavior of mask-wearing. Gender, grade, out-going history, father’s occupation, mother’s educational background, and the time filling out the survey were significantly associated with hand hygiene, whereas grade, mother’s educational background, and residence were associated with mask-wearing. The behaviors of hand-washing and mask-wearing among primary school students were influenced by gender, grade, and other factors, therefore, parents should make efforts of behavior guidance whereas governments should enlarge medium publicity.

Background: Type 2 diabetes mellitus is an expanding global public health issue, especially in developing countries. This study aimed to investigate the prevalence, awareness and control rate of type 2 diabetes mellitus, and assess its risk factors in elderly Chinese individuals.Methods: The health screening data of 376,702 individuals aged ≥ 65 years in Wuhan, China, were collected to analyse the prevalence, awareness, and control rates of diabetes. Indices, including fasting plasma glucose and other biochemical indicators, were measured for all participants using standard methods at the central laboratory. Multilevel logistic regression analysis was performed to assess the key determinants of the prevalence, awareness, and control rates of diabetes.Results: The prevalence, awareness, and control rates of diabetes in the Chinese individuals aged ≥ 65 years were 18.80%, 77.14%, and 41.33%, respectively. There were statistically significant differences in the prevalence, awareness, and control rates by gender. Factors associated with diabetes prevalence were age, body mass index (BMI), and central obesity; while those associated with awareness and control were gender, education level, marital status, physical activity, alcohol consumption, BMI, and central obesity.Conclusions: Diabetes is an important public health problem in the elderly in China. The awareness and control rates have improved, but overall remained poor. Therefore, effective measures to raise awareness and control the rates of diabetes should be undertaken to circumvent the growing disease burden in elderly Chinese people.

Objective: We aimed to systematically identify the possible risk factors responsible for severe cases.Methods: We searched PubMed, Embase, Web of science and Cochrane Library for epidemiological studies of confirmed COVID-19, which include information about clinical characteristics and severity of patients’ disease. We analyzed the potential associations between clinical characteristics and severe cases.Results: We identified a total of 41 eligible studies including 21060 patients with COVID-19. Severe cases were potentially associated with advanced age (Standard Mean Difference (SMD) = 1.73, 95% CI: 1.34–2.12), male gender (Odds Ratio (OR) = 1.51, 95% CI:1.33–1.71), obesity (OR = 1.89, 95% CI: 1.44–2.46), history of smoking (OR = 1.40, 95% CI:1.06–1.85), hypertension (OR = 2.42, 95% CI: 2.03–2.88), diabetes (OR = 2.40, 95% CI: 1.98–2.91), coronary heart disease (OR: 2.87, 95% CI: 2.22–3.71), chronic kidney disease (CKD) (OR = 2.97, 95% CI: 1.63–5.41), cerebrovascular disease(OR = 2.47, 95% CI: 1.54–3.97), chronic obstructive pulmonary disease (COPD) (OR = 2.88, 95% CI: 1.89–4.38), malignancy (OR = 2.60, 95% CI: 2.00–3.40), and chronic liver disease (OR = 1.51, 95% CI: 1.06–2.17). Acute respiratory distress syndrome (ARDS) (OR = 39.59, 95% CI: 19.99–78.41), shock (OR = 21.50, 95% CI: 10.49–44.06) and acute kidney injury (AKI) (OR = 8.84, 95% CI: 4.34–18.00) were most likely to prevent recovery. In summary, patients with severe conditions had a higher rate of comorbidities and complications than patients with non-severe conditions.Conclusion: Patients who were male, with advanced age, obesity, a history of smoking, hypertension, diabetes, malignancy, coronary heart disease, hypertension, chronic liver disease, COPD, or CKD are more likely to develop severe COVID-19 symptoms. ARDS, shock and AKI were thought to be the main hinderances to recovery.

At the end of 2019, the outbreak of coronavirus disease 2019 (COVID-19) in Wuhan was a serious threat to public health. This study aimed to evaluate the risk perception of COVID-19 among college students in China during the quarantine, explore its related factors, and provide reference for future study. This study invited college students from various provinces of China to participate in the survey through the Internet, and a total of 1,461 college students were included. T-test and analysis of variance were used to explore the relationship between demographic characteristics, social pressure, knowledge and risk perception. Multiple linear regression was used to identify factors associated with risk perception. This study shows that college students in China have high risk perception of COVID-19. Female college students (p<0.01), non-medical students (p<0.01), college students whose schools are located in Hubei (p = 0.01) and college students with higher knowledge level (p<0.01) have higher risk perception. Due to the strong infectivity and occult nature of COVID-19, it is necessary to improve the risk perception of college students through health education in various ways, and attention should be paid to some college students with low risk perception.

Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.