Department of Statistics, Faculty of Science, Comilla University, Cumilla, Bangladesh.
School of Mathematics, Physics, and Computing, Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, Queensland, Australia.
PLoS One. 2023 May 18;18(5):e0285940. doi: 10.1371/journal.pone.0285940. eCollection 2023.
Previous studies have shown a relationship between socio-demographic variables and the mental health of children and adolescents. However, no research has been found on a model-based cluster analysis of socio-demographic characteristics with mental health. This study aimed to identify the cluster of the items representing the socio-demographic characteristics of Australian children and adolescents aged 11-17 years by using latent class analysis (LCA) and examining the associations with their mental health.
Children and adolescents aged 11-17 years (n = 3152) were considered from the 2013-2014 Young Minds Matter: The Second Australian Child and Adolescent Survey of Mental Health and Wellbeing. LCA was performed based on relevant socio-demographic factors from three levels. Due to the high prevalence of mental and behavioral disorders, the generalized linear model with log-link binomial family (log-binomial regression model) was used to examine the associations between identified classes, and the mental and behavioral disorders of children and adolescents.
This study identified five classes based on various model selection criteria. Classes 1 and 4 presented the vulnerable class carrying the characteristics of "lowest socio-economic status and non-intact family structure" and "good socio-economic status and non-intact family structure" respectively. By contrast, class 5 indicated the most privileged class carrying the characteristics of "highest socio-economic status and intact family structure". Results from the log-binomial regression model (unadjusted and adjusted models) showed that children and adolescents belonging to classes 1 and 4 were about 1.60 and 1.35 times more prevalent to be suffering from mental and behavioral disorders compared to their class 5 counterparts (95% CI of PR [prevalence ratio]: 1.41-1.82 for class 1; 95% CI of PR [prevalence ratio]: 1.16-1.57 for class 4). Although children and adolescents from class 4 belong to a socio-economically advantaged group and shared the lowest class membership (only 12.7%), the class had a greater prevalence (44.1%) of mental and behavioral disorders than did class 2 ("worst education and occupational attainment and intact family structure") (35.2%) and class 3 ("average socio-economic status and intact family structure") (32.9%).
Among the five latent classes, children and adolescents from classes 1 and 4 have a higher risk of developing mental and behavioral disorders. The findings suggest that health promotion and prevention as well as combating poverty are needed to improve mental health in particular among children and adolescents living in non-intact families and in families with a low socio-economic status.
先前的研究表明,社会人口统计学变量与儿童和青少年的心理健康之间存在关系。然而,目前尚未发现基于模型的社会人口统计学特征聚类分析与心理健康之间的关系。本研究旨在通过潜在类别分析(LCA)确定澳大利亚 11-17 岁儿童和青少年社会人口统计学特征的项目聚类,并检查其与心理健康的关联。
本研究考虑了 2013-2014 年青年思想 Matters:第二次澳大利亚儿童和青少年心理健康和幸福感调查中年龄在 11-17 岁的儿童和青少年(n=3152)。基于三个层面的相关社会人口统计学因素进行潜在类别分析。由于精神和行为障碍的高患病率,使用具有对数链接二项式家族的广义线性模型(对数二项式回归模型)来检查识别类别的关联,以及儿童和青少年的精神和行为障碍。
本研究基于各种模型选择标准确定了五个类别。第 1 类和第 4 类呈现出脆弱类别,具有“最低社会经济地位和非完整家庭结构”和“良好社会经济地位和非完整家庭结构”的特征。相比之下,第 5 类表示最具特权的类别,具有“最高社会经济地位和完整家庭结构”的特征。对数二项式回归模型(未调整和调整模型)的结果表明,与第 5 类相比,属于第 1 类和第 4 类的儿童和青少年患精神和行为障碍的患病率约为 1.60 和 1.35 倍(PR[患病率比]的 95%CI[置信区间]:第 1 类为 1.41-1.82;第 4 类为 1.16-1.57)。尽管来自第 4 类的儿童和青少年属于社会经济上处于优势地位的群体,并且属于最低类别(仅占 12.7%),但该类别患精神和行为障碍的比例(44.1%)高于第 2 类(“最差的教育和职业成就以及完整的家庭结构”)(35.2%)和第 3 类(“平均社会经济地位和完整的家庭结构”)(32.9%)。
在五个潜在类别中,第 1 类和第 4 类的儿童和青少年患精神和行为障碍的风险更高。研究结果表明,需要促进和预防健康以及消除贫困,以改善特别是生活在不完整家庭和社会经济地位较低的家庭中的儿童和青少年的心理健康。