Mental health issues are rising continuously in young Australians. Experts agree that early intervention is essential to long-term wellbeing despite the fact that the reasons for the increase may not be evident. Previous studies have shown a relationship between socio-demographic factors with the mental health of children and adolescents. However, while utilizing a nationally representative sample, no study has yet been conducted a model-based cluster analysis of socio-demographic characteristics with mental illness. One of the major aims of this study is to identify the cluster of the items representing the socio-demographic characteristics of Australian children and adolescents using latent class analysis to differentiate vulnerable groups of people from the privileged group and then determine their associations with mental illness (yes/no), different types of mental health difficulties and uses of mental health services.
The study will use two datasets: Young Minds Matter (YMM): the Second Australian Child and Adolescent Survey of Mental Health and Wellbeing, a nationally representative cross-sectional data set, as well as Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC), a longitudinal dataset. Using longitudinal data the study will investigate whether and to what extent mental health levels of a child and their relationship to different social classes fluctuate and then compare this outcome with the results obtained from cross sectional data. Finally, and perhaps most importantly, this study will look at the relationship between children's mental health and cluster-based healthcare costs in order to predict cluster based excess healthcare costs related to mental illness.
Furthermore, it will examine whether expenditures rise over time as mental illness becomes more prevalent. Numerous regression techniques, like binary logistic regression, multivariate logistic regression and mixed modelling approach will be used to investigate the relationships. To identify significant number of latent classes, various model selection criterions, such as AIC, BIC, aBIC adjusted BIC, LR and entropy will be used.
The study's findings will benefit policy makers in such a way that significant impact of individual class will help government and non- government agencies to differentiate vulnerable class from the privileged class in formulating health-related policy. In addition, any significant relationship between cost of cluster based health services and mental health difficulties of children will help different scholars and researcher to find a technique of reducing health care cost of the specific group of peoples who expense more in taking mental health care services. Gaining knowledge from the characteristics of vulnerable group of people will help government policy makers to take special focus on that group of people as a pre-caution to reduce future economic burden.
For more information or zoom link, please email the Graduate Research School
or phone (07) 4631 1088. The zoom links are included in the ReDTrain Bulletin.