To examine a 16-year trend in cognitive impairment (CI) incidence and associated factors among older Chinese people. aged 60 and above whose cognitive function were normal at their first test. a secondary analysis that identified subjects from the database of Chinese Longitudinal Healthy Longevity Survey (CLHLS). The database contained mixed longitudinal cohorts of older Chinese people surveyed in 1998, 2000, 2002, 2005, 2008–09, 2011–12 and 2014. The cognitive function of subjects was tested using the Chinese Mini-Mental State Examination (CMMSE) in each wave. The unique individual code identified a mixed cohort of 17,896 subjects who had multiple CMMSE measures over a 16-year period and available covariates for the analysis. CI was defined as the CMMSE score below 18 points. Crude and age-standardised incidence of CI by gender were calculated by year of survey. Risk factor adjusted time trends in the incidence were examined using multilevel regression models. age-standardised CI incidence decreased from 58.77‰ to 10.09‰ (P < 0.001) from 1998 to 2014, and this decrease remained after adjusting for covariates. About 15.8% in the observed decline was explained by higher education, and 7.9% was due to health practice (regular exercise, physical activity and cognitive activity), beyond age and gender effects. the CI incidence among older Chinese people decreased from 1998 to 2014. Lower education level and less frequent health practices mentioned above were important risk factors in CI prevention.

Background: Postpartum depression (PPD) is a serious public health problem. Building a predictive model for PPD using data during pregnancy can facilitate earlier identification and intervention. Objective: The aims of this study are to compare the effects of four different machine learning models using data during pregnancy to predict PPD and explore which factors in the model are the most important for PPD prediction. Methods: Information on the pregnancy period from a cohort of 508 women, including demographics, social environmental factors, and mental health, was used as predictors in the models. The Edinburgh Postnatal Depression Scale score within 42 days after delivery was used as the outcome indicator. Using two feature selection methods (expert consultation and random forest-based filter feature selection [FFS-RF]) and two algorithms (support vector machine [SVM] and random forest [RF]), we developed four different machine learning PPD prediction models and compared their prediction effects. Results: There was no significant difference in the effectiveness of the two feature selection methods in terms of model prediction performance, but 10 fewer factors were selected with the FFS-RF than with the expert consultation method. The model based on SVM and FFS-RF had the best prediction effects (sensitivity=0.69, area under the curve=0.78). In the feature importance ranking output by the RF algorithm, psychological elasticity, depression during the third trimester, and income level were the most important predictors. Conclusions: In contrast to the expert consultation method, FFS-RF was important in dimension reduction. When the sample size is small, the SVM algorithm is suitable for predicting PPD. In the prevention of PPD, more attention should be paid to the psychological resilience of mothers.

Background: China has been experiencing the largest rural to urban migration in history. Rural-to-urban migrants are those who leave their hometown for another place in order to work or live without changing their hukou status, which is a household registration system in China, categorizing people as either rural residents or urban residents. Rural-to-urban migrants typically find better job opportunities in destination cities, and these pay higher salaries than available in their home regions. This has served to improve the enrollment rates in the New Cooperative Medical Scheme (NCMS) of rural families, protecting households from falling into poverty due to diseases. However, current regulations stipulate that people who are registered in China's rural hukou can only participate in their local NCMS, which in turn poses barriers when migrants seek medical services in the health facilities of their destination cities. To examine this issue in greater depth, this study examined the associations between migration, economic status of rural households, and NCMS enrollment rate, as well as NCMS utilization of rural-to-urban migrants. Methods: A multistage cluster sampling procedure was adopted. Our sample included 9,097 households and 36,720 individuals. Chi-square test and T-test were used to examine differences between the two populations of migrants and non-migrants based on age, gender, marriage status, and highest level of education. Ordinal logistic regression was used to examine the association between migration and household economic status. Binary logistic regression was used to examine the associations between household economic status, migration and enrollment in the NCMS. Results: Migration was positively associated with improved household economic status. In households with no migrants, only 11.3% of the population was in the richest quintile, whereas the percentage was more than doubled in households with family members who migrated in 2006. Among those using in-patient medical services, 54.3% of migrants in comparison with 17.5% of non-migrants used out-of-county hospitals, many of which were not designated hospitals (Designated hospitals refer to hospitals where, if people use in patient health care, could receive reimbursement from the NCMS.); and 55.2% of migrants in comparison with 24.6% of non-migrants, who had the NCMS in 2006, received no reimbursement from the NCMS. The three main reasons of not receiving reimbursement were: staying in a hospital not designated by the NCMS, lack of knowledge of NCMS policies, and encountering difficulties obtaining reimbursement. Conclusion: Migrants to urban centers improve the economic status of their rural household economic of origin. However, obtaining reimbursement under the current NCMS for the cost of hospital services provided by undesignated providers in urban centers is limited. Addressing this challenge is an emerging policy priority.

Few studies have focused on depression and social support in Eastern populations, especially women in rural China. Our research investigated depression among women in rural China, and studied the relationships between social support and depression. We recruited women ages 16 years and older from north Sichuan. Participants completed socio-demographic measures, the Center for Epidemiologic Studies Depression Scale, and the Duke Social Support Index. The analysis method included descriptive statistics and logistic regression. The final sample included 1,898 participants with a mean age of 48.6 years, and the prevalence of significant depressive symptoms was 12.4%. Results suggest being unemployed, having poorer perceived health/economic status, and lower social support were positively associated with depression. Younger age and greater social support were negatively associated with depression. This study provides insights on the psychological health of women in rural China and potential directions for future research. These issues are especially pertinent during this time of rapid economic transformation and outmigration in rural China.