Exploring associations of adverse childhood experiences with patterns of 11 health risk behaviors in Chinese adolescents: focus on gender differences

Child and adolescent psychiatry and mental health(2023)

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Abstract
Purpose Adolescents exposed to adverse childhood experiences (ACEs) are at increased risk for health-compromising behaviors. However, few studies have investigated how ACEs correlate with patterns of health risk behaviors (HRBs) during adolescence, a crucial developmental period. The aim was to extend the current knowledge about the relationship between ACEs and HRB patterns among adolescents, and to explore gender differences. Methods A multi-centered population-based survey was conducted in 24 middle schools in three provinces across China between 2020 and 2021. A total of 16,853 adolescents effectively completed anonymous questionnaires covering exposure to eight ACE categories and 11 HRBs. Clusters were identified using latent class analysis. Logistic regression models were utilized to test the association between them. Results There were four classes of HRB patterns: “Low all” (58.35%), “Unhealthy lifestyle” (18.23%), “Self-harm” (18.42%), and “High all” (5.0%). There were significant differences between HRB patterns in terms of the different numbers and types of ACEs in three logistic regression models. Specifically, compared to “Low all,” different types of ACEs were positively associated with the three other HRB patterns, and there were significant trends toward increase in the three latent classes of HRBs with higher ACEs. In general, females with ACEs had a higher risk of “High all” except sexual abuse than males. Conclusion Our study comprehensively considers the association between ACEs and aggregation categories of HRBs. The results support efforts to improve clinical healthcare, and future work may explore protective factors based on individual, family, and peer education to mitigate the negative trajectory of ACEs.
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Key words
Adverse childhood experiences,Health risk behaviors,Adolescent,Gender,Latent class analysis
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