Longitudinal associations between specific types/amounts social contact and cognitive function among middle-aged and elderly Chinese: A causal inference and longitudinal targeted maximum likelihood estimation analysis.

BACKGROUND: Social contact has demonstrated associations with cognitive function, while the literature on the effect of specific social relationship subdomains on cognitive function is limited. This study aimed to examine the causal effects of specific types/amounts of social contact on cognitive function among middle-aged and elderly Chinese. METHODS: A total of 38,883 middle-aged and elderly adults from the China Health and Retirement Longitudinal Study were involved. Social contact in this study included interaction with families, taking care of grandchildren, interaction with friends, and participation in three types of social activities. We performed the linear mixed-effects model analysis with propensity score approach and the longitudinal targeted maximum likelihood-based estimation analysis after adjusting for potential covariates and confounders. RESULTS: Interaction with families, caring for grandchildren, interaction with friends and participation in social activities were all associated with cognitive z-scores. Participants who interacted with families "2-3 times a week" and "once a week" versus "almost every day" had higher cognitive z-scores. Those who interacted with friends and participated in social activities "almost every week" versus "almost daily" had higher cognitive z-scores. LIMITATIONS: The assessment of cognition was biased against people with poor education due to elements of language and mathematical testing, and against those with visual impairment. CONCLUSIONS: Social contact was associated with better cognitive function among middle-aged and elderly Chinese. Social contact "1-3 times a week" was optimal for cognitive function. More social contact in middle-aged and elderly Chinese led to less cognitive decline in later life than in their inactive peers.

Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database.

Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599-0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.

Long-Time Trend of Colorectal Cancer Mortality Attributable to High Processed Meat Intake in China and a Bayesian Projection from 2020 to 2030: A Model-Based Study.

Colorectal cancer is among the leading causes of cancer worldwide. Processed meat was known to be positively associated with a higher risk of gastrointestinal cancer. This study focused on the long-time trends of colorectal cancer mortality attributable to high processed meat intake in China from 1990 to 2019 and the projection for the next decade based on data obtained from the Global Burden of Disease 2019 study. We used an age-period-cohort model to fit the long-time trend. The joinpoint model was conducted to estimate the average and annual change of the attributable mortality. The Bayesian age-period-cohort model was used to project the crude attributable mortality from 2020 to 2030. An upward trend in colorectal cancer mortality attributable to high processed meat intake was observed for both sexes in China from 1990 to 2019, with an overall net drift of 4.009% for males and 2.491% for females per year. Projection analysis suggested that the burden of colorectal cancer incidence and mortality would still be high. Our findings suggested that colorectal cancer death attributable to high processed meat intake is still high in China, and elderly males were at higher risk. Gradually decreasing the intake of processed meat could be an effective way to reduce colorectal cancer mortality.

The association between social engagement and depressive symptoms in middle-aged and elderly Chinese: A longitudinal subgroup identification analysis under causal inference frame.

Background: Studies have suggested that there is a significant association between social engagement and depression symptoms. However, this association may differ in people with different features such as different sociodemographic characteristics and health conditions. Methods: Research data were obtained from the CHARLS database. The causal inference was performed with the propensity score. We used the linear mixed-effects model tree algorithm under the causal inference frame for subgroup identification analysis. Results: We included 13,521 participants, and the median follow-up time is 4 years. Under the casual inference frame, the association between social engagement and depression symptoms is confirmed for all included individuals (OR = 0.957, P = 0.016; 95%CI: 0.923-0.992). Using the linear mixed-effects model tree, we found two subgroups, including middle-aged and elderly residents who live in rural areas with <6 h of sleep and those living in urban areas, could benefit more from social engagement. After using the propensity score method, all the two subgroups selected are statistically significant (P = 0.007; P = 0.013) and have a larger effect size (OR = 0.897, 95%CI: 0.830-0.971; OR = 0.916, 95%CI: 0.854-0.981) than the whole participants. As for sex difference, this associations are statistically significant in male (OR: 0.935, P = 0.011, 95%CI: 0.888-0.985) but not in female (OR: 0.979, P = 0.399, 95%CI: 0.931-1.029). Conclusions: Our findings indicate that social engagement may reduce the risks of depressive symptoms among all individuals. The identified subgroups of middle-aged and elderly residents who live in rural areas with <6 h of sleep and those who live in urban areas may benefit more from the social engagement than the whole participants.