Socio-demographic factors and mental health trajectories in Australian children and primary carers: Implications for policy and intervention using latent class analysis

Abstract

Children's mental health status (MHS) is frequently influenced by their primary carers (PCs), underscoring the significance of monitoring disparities longitudinally. This research investigated the association between socio-demographic clusters and mental health trajectories among children and their PCs over time. Data from waves 6-9c2 of the Longitudinal Study of Australian Children (LSAC) were analyzed using Latent Class Analysis (LCA) to identify four socio-demographic classes among children aged 10–11 years at wave 6. Multinomial logistic regression and predictive marginal analysis explored associations between classes and mental health outcomes. PCs in Class 4 (disadvantaged and separated families with indigenous children) exhibited higher odds of borderline and abnormal MHS compared to Class 1 (prosperous and stable working families) across all waves. However, while MHS of PCs' impacted children consistently, the association with socio-demographic classes was significant only in wave 6. Class 4 children had elevated risks of mental illness compared to Class 1, while Class 3, characterized by educated working mothers, had lower risks. Reducing mental health risks entails addressing socio-economic disparities, supporting stable family structures, and offering tailored interventions like counseling and co-parenting support. Longitudinal monitoring and culturally sensitive approaches are crucial for promoting mental well-being across diverse groups.