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Childhood trauma (CT) increases rates of psychiatric disorders and symptoms, however, the lasting effect of CT into adulthood has little exploration using large-scale samples.
Objectives
This study estimated the prevalence of CT in a large sample of Chinese young adults, examining the risk factors of current psychological symptoms among those with CT experiences.
Methods
117,769 college students were divided into CT and non-CT groups. The propensity score matching method balanced the confounding sociodemographic factors between the two groups, compared to 16 self-reported psychiatric disorders (e.g., depression, anxiety, eating disorder, obsessive-compulsive disorder, autism, social anxiety disorder, post-traumatic stress disorder), and seven current psychiatric symptoms. Hierarchical regression employed the significant risk factors of the seven current psychiatric symptoms.
Results
The prevalence of CT among young adults was 28.76% (95% CI: 28.47–29.04%). Youths with CT experiences reported higher psychiatric disorder rates and current symptom scores (P < 0.001). Sociodemographic factors (females, family disharmony, low socioeconomic status, poor relationship with parents, lower father’s education level) and lifestyle factors (smoking status, alcohol consumption, lack of exercise) were significantly associated with current psychiatric symptoms.
Results
Public health departments and colleges should develop strategies to promote mental health among those who have experienced CT.
In this chapter we introduce developing and interpreting multilevel models. We first define multilevel models and explore how this approach is an improvement on disaggregation and aggregation of data across multiple levels. We then work through four different multilevel models. We provide examples of what kinds of questions can be answered by each model and how to interpret the statistical output. We then explore some additional issues in fitting multilevel models in Stata and consider additional applications of multilevel models.
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