Depression and associated factors in internal migrant workers in China

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.

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.

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.

Among 1180 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.

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.