Use of latent profile analysis and k-means clustering to identify student anxiety profiles

Abstract

Background: Anxiety disorders are often the first presentation of psychopathology in youth and are considered the most common psychiatric disorders in children and adolescents. This study aimed to identify distinct student anxiety profiles to develop targeted interventions. Methods: A cross-sectional study was conducted with 9738 students in Yingshan County. Background characteristics were collected and Mental Health Test (MHT) were completed. Latent profile analysis (LPA) was applied to define student anxiety profiles, and then the analysis was repeated using k-means clustering. Results: LPA yielded 3 profiles: the low-risk, mild-risk and high-risk groups, which comprised 29.5, 38.1 and 32.4% of the sample, respectively. Repeating the analysis using k-means clustering resulted in similar groupings. Most students in a particular k-means cluster were primarily in a single LPA-derived student profile. The multinomial ordinal logistic regression results showed that the high-risk group was more likely to be female, junior, and introverted, to live in a town, to have lower or average academic performance, to have heavy or average academic pressure, and to be in schools that have never or occasionally have organized mental health education activities. Conclusions: The findings suggest that students with anxiety symptoms may be categorized into distinct profiles that are amenable to varying strategies for coordinated interventions.