Economic development, weak ties, and depression: Evidence from China

Depressive disorder benefits of cities: Evidence from the China

BACKGROUND

Rapid urbanization is a major trend in global population migration. There is growing debate about whether this urban-rural disparity exacerbate depression at the individual level. This study aims to investigate how urban living has a beneficial impact on individual mental health.

METHODS

Based on the data of 15,764 participants in the 2018 China Health and Retirement Longitudinal Study (CHARLS), we perform analysis of variance to identify the gap in depression levels between urban and rural areas. Extensive comparisons and detailed statistical analyses are carried out to demonstrate the differences in social participation between urban and rural residents. Finally, we conduct a series of mediation and moderation analyses to reveal the underlying mechanisms of depressive disorder benefits of cities.

RESULTS

The results indicate that those who lived in urban areas were less likely to suffer from depression (β = -1.461, 95 % CI = [-1.691, -1.235], p < 0.001). Social engagement is found to mediate the relationship between residence type (β = 0.164, 95 % CI = [0.136, 0.193], p < 0.001) and individual depression (β = -0.462, 95 % CI = [-0.587, -0.337], p < 0.001). City size plays a moderating role in the association between urban living and social engagement.

LIMITATIONS

The mechanism is conducted through cross-sectional data. Self-reported depression status is accessed in this study, which could lead to measurement error.

CONCLUSION

This study demonstrates the beneficial effects of urban living on individual depression, and reveals the mechanism by which urbanization at different scales affects the prevalence of depression.

The influence of lifestyle habits on levels of depression among rural middle school students in Northeastern China

Background

Depression rates among adolescents have risen dramatically over the past decade. Therefore, preventing depression among adolescents is particularly important. Differences in lifestyle habits may play a role in depression.

Purpose

This study aimed to explore the influence of living habits on depression levels among rural middle school students in Northeast China and to provide a theoretical basis for developing interventions to reduce depression levels in middle school students.

Methods

A total of 296 middle school students aged 13-15 years from Benxi City, Northeast China completed the anthropometric measurements, Physical Activity Scale-3 (PARS-3), and the Self-Rating Depression Scale (SDS). Their average screen time in the most recent week, parents' education level, and monthly family income were collected through a questionnaire.

Results

Females had higher depression scores than males (41.0 ± 6.9 vs. 37.9 ± 8.0). Physical activity (β = -0.38, t = -7.06, P < 0.01), family income (β = -0.20, t = -4.07, P < 0.01), screen time (β = 0.16, t = 3.34, P < 0.01), age (β = 0.15, t = 3.16, P < 0.01), sex (β = -0.13, t = -2.74, P < 0.01), and sleep quality (β = -0.08, t = -1.87, P < 0.01) are important factors related to depression levels.

Conclusion

The preliminary analysis results showed that among middle school students in rural Northeast China, the depression level of females was significantly higher than that of males. Poor quality sleep, low levels of physical activity, low household income, and long screen time were positively associated with depression. Therefore, strengthening physical activity, improving sleep quality, and reducing screen time are of clinical relevance in preventing and reducing depression.

Depression prediction based on LassoNet-RNN model: A longitudinal study

Depression has become a widespread health concern today. Understanding the influencing factors can promote human mental health as well as provide a basis for exploring preventive measures. Combining LassoNet with recurrent neural network (RNN), this study constructed a screening model ,LassoNet-RNN, for identifying influencing factors of individual depression. Based on multi-wave surveys of China Health and Retirement Longitudinal Study (CHARLS) dataset (11,661 observations), we analyzed the multivariate time series data and recognized 27 characteristic variables selected from four perspectives: demographics, health-related risk factors, household economic status, and living environment. Additionally, the importance rankings of the characteristic variables were obtained. These results offered insightful recommendations for theoretical developments and practical decision making in public health.