Influenza is a contagious disease with high transmissibility to spread around the world with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Few mathematical models can be used because influenza incidence data are generally not normally distributed. We developed a mathematical model using Extreme Value Theory (EVT) to forecast the probability of outbreak of highly pathogenic influenza. The incidence data of highly pathogenic influenza in Zhejiang province from April 2009 to November 2013 were retrieved from the website of Health and Family Planning Commission of Zhejiang Province. MATLAB “VIEM” toolbox was used to analyze data and modelling. In the present work, we used the Peak Over Threshold (POT) model, assuming the frequency as a Poisson process and the intensity to be Pareto distributed, to characterize the temporal variability of the long-term extreme incidence of highly pathogenic influenza in Zhejiang, China. The skewness and kurtosis of the incidence of highly pathogenic influenza in Zhejiang between April 2009 and November 2013 were 4.49 and 21.12, which indicated a “fat tail” distribution. A QQ plot and a mean excess plot were used to further validate the features of the distribution. After determining the threshold, we modeled the extremes and estimated the shape parameter and scale parameter by the maximum likelihood method. The results showed that months in which the incidence of highly pathogenic influenza is about 4462/2286/1311/487 are predicted to occur once every five/three/two/one year, respectively. Despite the simplicity, the present study successfully offers the sound modeling strategy and a methodological avenue to implement forecasting of an epidemic in the midst of its course.

Previous studies have shown that middle-aged and elderly adults with disabilities are at higher risk for depressive symptoms. However, there are few studies on the long-term trajectories of depressive symptoms in the Chinese middle-aged and elderly disabled population. This study aimed to identify the different development trajectories of depressive symptoms and their influencing factors in middle-aged and elderly people with disabilities in China. Using data from the China Health and Retirement Longitudinal Study (CHARLS) in 2011, 2013, 2015, and 2018, a longitudinal cohort was formed for the study. A total of 2053 participants underwent at least two measures of depressive symptoms, assessed using the Center for Epidemiological Studies Depression Scale (CES-D10), a depression symptom assessment scale. We constructed a Group-Based Trajectory Model (GBTM) to identify the development trajectory of depressive symptoms in 2053 middle-aged and elderly disabled individuals, screened the potential predictors using lasso regression, and analyzed the factors affecting the development trajectory of depression through multivariate logistic regression. We identified four depression symptom trajectories throughout the follow-up process: “low depressive symptom group”, “worsening depressive symptom group”, “relieved depressive symptom group”, and “high depressive symptom group”. We found that there were differences in basic characteristics among different subgroups of depression trajectory. However, middle-aged and elderly disabled women living in rural areas, with limited ADL or IADL, physical pain, poor self-reported health and self-reported memory, short sleep time, and no relatives and friends to take care of them were the key groups for the prevention and treatment of depressive symptoms. There is heterogeneity in the trajectories of depressive symptoms in the Chinese middle-aged and elderly disabled population, it is necessary to focus on the characteristics of the trajectories of different subgroups.

The global aging phenomenon has raised concerns about the cognitive abilities of older individuals. This study aimed to explore the relationship between social participation, depressive symptoms, and cognitive function among middle-aged and older adults. This study utilized data from the China Longitudinal Study of Health and Retirement (CHARLS) from wave 1 to wave 4. We used linear regression and generalized estimation equations to investigate the correlation between social participation, depressive symptoms, and cognitive function. Moreover, three models were constructed by adjusting covariates, and we used the sobel test and bootstrap method to analyze the mediating effects of depressive symptoms on social activities and cognitive function. The results of both linear regression and generalized estimation equation showed that social participation had a positive correlation with cognitive function (P < 0.05), and the impact of social participation on cognition increased with the number of social activity types. Meanwhile, depressive symptoms had a negative association with cognitive function (P < 0.05). Furthermore, there was no interaction between social participation and depressive symptoms on cognitive function. Finally, after adjusting the model, social participation could affect cognitive function by affecting depressive symptoms (P < 0.05). The study emphasizes the mediating role of depressive symptoms in the relationship between social participation and cognitive function. Notably, no interaction was observed between social participation and depressive symptoms. These findings highlight the potential of active social participation in reducing depressive symptoms and enhancing cognitive function in middle-aged and older adults.

Background: Influenza is associated with a large number of disease burdens, and it is generally recommended that all healthcare workers (HCWs) get an influenza vaccination. However, the vaccination rates among HCWs are still low. This study aimed to assess HCWs' knowledge, attitude, and practice (KAP) about the influenza vaccine, and by establishing a structural equation model (SEM) to explore the influencing factors of medical personnel's influenza vaccination in Chongqing, China.Methods: From September to November 2019, we conducted a cross-sectional survey in four sentinel hospitals and four non-sentinel hospitals in Chongqing, China. We calculated knowledge, attitude, and behavior scores for each study participant and assessed the level of knowledge, attitude, and behavior of the medical staff. An SEM was used to evaluate the relationship between latent variables, and the path graph between knowledge, attitude, and behavior was established.Results: A total of 1,412 valid questionnaires were collected in this survey, including four sentinel hospitals (N = 606, 42.92%) and four non-sentinel hospitals (N = 806, 57.08%). Women (N = 1,102, 78.05%) were more than men (N = 310, 21.95%), with an average age of 32.36 ± 7.78 years old and under 30 years old (N = 737, 52.20%), respectively. Nurses (741, 52.48%) were the main subjects, followed by physicians (457, 32.37%). The final SEM model was obtained after the model was modified and adjusted. A bootstrap analysis of path coefficients was carried out on the final model. Knowledge has a direct influence on behavior. The normalized path coefficient is 0.071 (95% CI: 0.002–0.161), and the value of P of the hypothesis test result of the path coefficient is 0.042. The direct influence of knowledge on attitude standardization was 0.175 (95% CI: 0.095–0.281). The direct influence of attitude on practice standardization was 0.818 (95% CI: 0.770–0.862). The indirect effect of knowledge on the standardization of practice through attitude was 0.144 (95% CI: 0.076–0.235).Conclusions: According to the SEM, there is a direct positive correlation between KAP and the influenza vaccine. The indirect influence of knowledge on the standard of behavior through attitude is about two times as much as the direct influence on behavior, indicating that attitude plays a strong mediating role between knowledge and practice.

Health-related quality of life (HRQOL) has become commonly used both as a concept and as a field of research. However, little is known about the HRQOL of men who have sex with men and women (MSMW). The aim of this study was to examine HIV-related behaviors, social support, and HRQOL status and explore its predictors among MSMW. An anonymous cross-sectional study was conducted by snowball sampling method in 2013. A total of 563 Chinese MSM completed a structured questionnaire. The HRQOL and social support were measured with the Chinese version of the World Health Organization Quality of Life Scale (WHOQOL-BRFE) and the Social Support Rating Scale (SSRS), respectively. Of the 563 MSM analyzed, 77 (13.68%) were MSMW who had a higher proportion of in-marriage and preference for an insertive role as compared with the men who have sex with men only (MSMO) (P<0.05). As high as 70.13% of MSMW had no regular sex partners and 72.73% of MSMW reported engaging in unprotected anal sex in the last six months. 36.36% had tested for HIV, while only 12.99% had accepted HIV voluntary counseling and testing (VCT) services. The scores of objective support and subjective support in MSMW were significantly higher than that of MSMO (P<0.05). No statistically significant difference was found in scores of all the four domains of the HRQOL between MSMW and MSMO. When comparing the HRQOL scores of MSMW with the Chinese general population reference group, the scores of MSMW were significantly lower in physical health domain. In a multivariate regression model, age, monthly income, sexual role, VCT acceptability, subjective support were associated with variability in HRQOL. To improve the HRQOL among MSMW, more attention needs to be paid to those with low social support, low-income, the old and those prefer a receptive role during anal sex populations.

Background: Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults. Methods: We conduct a cross-sectional survey with convenience sampling in this study. We use a questionnaire From January 2019 to December 2019 to collect data on disease history and adults’ living habits who got dual-energy X-ray absorptiometry. We established the prediction models of osteoporosis in three steps. Firstly, we performed feature selection to identify risk factors related to osteoporosis. Secondly, the qualified participants were randomly divided into a training set and a test set in the ratio of 7:3. Then the prediction models of osteoporosis were established based on Artificial Neural Network (ANN), Deep Belief Network (DBN), Support Vector Machine (SVM) and combinatorial heuristic method (Genetic Algorithm - Decision Tree (GA-DT)). Finally, we compared the prediction models’ performance through accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to select the optimal prediction model. Results: The univariate logistic model found that taking calcium tablet (odds ratio [OR] = 0.431), SBP (OR = 1.010), fracture (OR = 1.796), coronary heart disease (OR = 4.299), drinking alcohol (OR = 1.835), physical exercise (OR = 0.747) and other factors were related to the risk of osteoporosis. The AUCs of the training set and test set of the prediction models based on ANN, DBN, SVM and GA-DT were 0.901, 0.762; 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. After evaluating four prediction models’ performance, we selected a three-layer back propagation neural network (BPNN) with 18, 4, and 1 neuron in the input layer, hidden and output layers respectively, as the optimal prediction model. When the probability was greater than 0.330, osteoporosis would occur. Conclusions: Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research.