Temporal trends in youth mental health: Insights and predictions from 20 years of Child and Adolescent Mental Health Services data

Abstract

Objectives:

There is a well-established trend of increasing prevalence of mental health disorders among children and young people. Understanding patterns across diagnostic categories and predicting future changes is crucial for effective interventions and service planning.

Methods:

We employed advanced time series analysis techniques, autoregressive integrated moving average-based time series modelling and forecasting, to analyse two decades of routinely collected data from the Western Australian Child and Adolescent Mental Health Services system. The large-scale dataset, with consistent sampling intervals, enabled robust time series analyses to account for secular, seasonal and random fluctuations. Models estimated both historical and forecasted future trends in mental health presentations at Western Australian Child and Adolescent Mental Health Services.

Results:

Modelling of historical data from 2004 to 2024 shows significant increases for anxiety disorders, mood disorders, personality disorders, sleep disorders, attention deficit hyperactivity disorder (ADHD) and autism and eating disorders. Forecasting to 2044 suggests that while anxiety disorders will decrease, ADHD, autism, eating disorders and sleep disorders will continue to increase.

Conclusions:

We have established autoregressive integrated moving average modelling and forecasting as a robust, sophisticated and useful statistical approach to characterising historical and future trends in youth mental health. The ability to forecast into the future with confidence means we can identify what services are most needed and where gaps exist in current service provision or fund distribution permitting strategic allocation of finite resources and supporting complex funding decisions. Importantly, our findings encourage other health care services, locally and internationally, to use autoregressive integrated moving average modelling and forecasting to capitalize on routine health data to support proactive service planning initiatives.