Background Internal migrant workers are a large population in China. Current health related studies among this population mainly focused on infectious disease, maternal health and occupational diseases and injuries. However, very limited studies were paid attention to mental health of migrant workers though it is an important public health issue. Aims The current study aims to understand prevalence of depression symptoms and factors associated with depression among Chinese migrant workers using novel methods to develop a comprehensive sample. Methods Respondent-driven sampling (RDS) was employed to recruit the target population, who are required 1) not to hold a hukou indicative of living in central areas or near suburbs of Chengdu city; 2) to be 16 years or older; 3) not to be a student. The Center for Epidemiologic Depression Scale (CES-D) was used to measure depression symptoms of migrant workers. And then Structural Equation Model (SEM) was applied to explore factors associated with depression among Chinese migrant workers. Results Among 1,180 migrant workers, 23.7% of them had clinically relevant depression symptoms (CES-D score >= 16), and 12.8% were consistent with a clinical diagnosis of depression (CES-D score >= 21). Self-rated economic status, city adaptation status, and self-rated health had negative effects on depression. Social economic status (SES) affected depression, and was mediated by self-rated economic status and self-rated health. City adaptation status was affected by length of residence in the city, satisfaction with one’s job, and the social support that one could obtain while living in the city. Conclusions The findings indicated a higher prevalence of depression symptoms among migrant workers comparing to general population reported by previous studies, identified possible factors associated with depression symptoms, and also explored relationships between these factors. Our study provides a model to understand mental health of Chinese internal migrant workers and to generate important research questions for the future.

This retrospective cohort study attempts to investigate pregnancy complications and adverse pregnancy outcomes in women of advanced maternal age (AMA). Data were extracted from electronic medical records system at West China Second University Hospital of Sichuan University from January 2013 to July 2016. The study cohort consisted 8 subgroups of women on 4 different age levels (20–29 years, 30–34 years, 35–39 years and ≥40 years) and 2 different parities (primiparity and multiparity). In the study period, 38811 women gave birth at our hospital, a randomized block was used to include 2800 women of singleton pregnancy >28 gestational weeks, with 350 patients in each subgroup. Maternal complications and fetal outcomes were collected and defined according to relevant guidelines. Confounding factors representing maternal demographic characteristics were identified from previous studies and analysed in multivariate analysis. There was an increasing trend for the risks of adverse pregnancy outcomes with increasing age, especially in AMA groups. Our study showed that AMA, primiparity, maternal overweight or obesity, lower educational level and residence in rural area increased pregnancy complications and adverse fetal outcomes. Increased professional care as well as public concern is warranted.

ChiCTR-IOR-16008109.

BackgroundChina 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.MethodsA 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.ResultsMigration 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.ConclusionMigrants 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.

Circadian disruption is a risk factor for metabolic, psychiatric and age-related disorders, and non-human primate models could help to develop therapeutic treatments. Here, we report the generation of BMAL1 knockout cynomolgus monkeys for circadian-related disorders by CRISPR/Cas9 editing of monkey embryos. These monkeys showed higher nocturnal locomotion and reduced sleep, which was further exacerbated by a constant light regimen. Physiological circadian disruption was reflected by the markedly dampened and arrhythmic blood hormonal levels. Furthermore, BMAL1-deficient monkeys exhibited anxiety and depression, consistent with their stably elevated blood cortisol, and defective sensory processing in auditory oddball tests found in schizophrenia patients. Ablation of BMAL1 up-regulated transcriptional programs toward inflammatory and stress responses, with transcription networks associated with human sleep deprivation, major depressive disorders, and aging. Thus, BMAL1 knockout monkeys are potentially useful for studying the physiological consequences of circadian disturbance, and for developing therapies for circadian and psychiatric disorders.

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.

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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.