Developmental trajectories of outpatient mental health service contact from childhood to early adulthood in an Australian birth cohort - Ogilvie, James M.; Crissman, Belinda; Hurren, Emily; Thompson, Carleen M.; Kuluk, Aydan; Allard, Troy; Broidy, Lisa; Dennison, Susan; Kisely, Steve; Stewart, Anna (Frontiers in Psychiatry, 2026)
BackgroundDespite increasing empirical support for primary intervention in the early development of mental disorders, limited lifespan developmental research exists to identify the potential timing of such interventions. This study takes a novel approach by applying Group Based Trajectory Modelling (GBTM) to identify patterns of outpatient mental health service (MHS) contacts over the early life-course. GBTM is a statistical method that can assist in identifying subgroups in a population that follow similar developmental trajectories over time.MethodsWe analyzed longitudinal (age ≈10 to 23 years) outpatient MHS contact data for an Australian birth cohort (N = 5,359) using GBTM. We examined variation across trajectory groups by sex, Indigenous status (non-Indigenous/Indigenous Australians), psychiatric diagnoses associated with hospital admissions, place of residence (metropolitan, regional, remote), and socioeconomic status.ResultsFour distinct trajectories of MHS contacts were identified, including escalating (5.8%), low (64.5%), adolescent limited (14.5%), and childhood peak declining (15.3%) trajectories. The escalating contacts trajectory group contained the fewest individuals (n = 308) but accounted for the highest proportion of contacts (32.3%), providing evidence of the need for and potential value of early identification and intervention for these individuals. Variations were also noted across trajectory groups for place of residence, socioeconomic status, and diagnosed mental disorders from hospital admissions.ConclusionsGBTM can assist examination of variations in MHS contacts across the life course. Efficacious intervention with a small but distinct group of vulnerable individuals may meaningfully impact available system resources and improve their outcomes.