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BACKGROUND As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to an individual’s risk of illness progression. The application of staging has been traditionally limited to trained clinicians, yet if digital technologies could be leveraged to apply clinical staging, then this could increase the scalability and utility of this model in services. OBJECTIVE The aim of this study is to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders. METHODS The cohort comprised 131 young people, aged between 16 to 25 years, who presented to youth mental health services in Australia for the first time between November 2018 to March 2021. Clinical stages (either stage 1a or stage 1b+) were allocated independently by expert psychiatrists and compared to the digital algorithm based on a multidimensional self-report questionnaire. RESULTS Of the 131 participants, the mean (SD) age was 20.3 (2.4) years and 94 (71.8%) were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the expert ratings with a substantial interrater agreement (κ=0.67, P CONCLUSIONS This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in early stages of common anxiety and depressive disorders. Between 11% and 27% of young people presenting for care may be suitable for low intensity online or brief interventions, creating additional clinical capacity to be directed towards those who are in stage 1b+ for further assessment and intervention.

BACKGROUND As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to an individual’s risk of illness progression. The application of staging has been traditionally limited to trained clinicians, yet if digital technologies could be leveraged to apply clinical staging, then this could increase the scalability and utility of this model in services. OBJECTIVE The aim of this proof-of-concept study is to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders. METHODS The cohort comprised 131 young people, aged between 16 to 25 years, who presented to youth mental health services in Australia for the first time between November 2018 to March 2021. Clinical stages (either stage 1a or stage 1b+) were allocated independently by expert psychiatrists and compared to the digital algorithm based on a multidimensional self-report questionnaire. RESULTS Of the 131 participants, the mean (SD) age was 20.3 (2.4) years and 94 (71.8%) were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the expert ratings with a substantial interrater agreement (κ=0.67, P CONCLUSIONS This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in early stages of common anxiety and depressive disorders. Between 11% and 27% of young people presenting for care may be suitable for low intensity online or brief interventions, creating additional clinical capacity to be directed towards those who are stage 1b+ for further assessment and intervention.

Clinical staging proposes that youth-onset mental disorders develop progressively, and that active treatment of earlier stages should prevent progression to more severe disorders. This retrospective cohort study examined the longitudinal relationships between clinical stages and multiple clinical and functional outcomes within the first 12 months of care.Demographic and clinical information of 2901 young people who accessed mental health care at age 12-25 years was collected at predetermined timepoints (baseline, 3 months, 6 months, 12 months). Initial clinical stage was used to define three fixed groups for analyses (stage 1a: 'non-specific anxious or depressive symptoms', 1b: 'attenuated mood or psychotic syndromes', 2+: 'full-threshold mood or psychotic syndromes'). Logistic regression models, which controlled for age and follow-up time, were used to compare clinical and functional outcomes (role and social function, suicidal ideation, alcohol and substance misuse, physical health comorbidity, circadian disturbances) between staging groups within the initial 12 months of care.Of the entire cohort, 2093 young people aged 12-25 years were followed up at least once over the first 12 months of care, with 60.4% female and a baseline mean age of 18.16 years. Longitudinally, young people at stage 2+ were more likely to develop circadian disturbances (odds ratio [OR]=2.58; CI 1.60-4.17), compared with individuals at stage 1b. Additionally, stage 1b individuals were more likely to become disengaged from education/employment (OR=2.11, CI 1.36-3.28), develop suicidal ideations (OR=1.92; CI 1.30-2.84) and circadian disturbances (OR=1.94, CI 1.31-2.86), compared to stage 1a. By contrast, we found no relationship between clinical stage and the emergence of alcohol or substance misuse and physical comorbidity.The differential rates of emergence of poor clinical and functional outcomes between early versus late clinical stages support the clinical staging model's assumptions about illness trajectories for mood and psychotic syndromes. The greater risk of progression to poor outcomes in those who present with more severe syndromes may be used to guide specific intervention packages.

Objective This study utilised digital technology to assess the clinical needs of young people presenting for care at headspace centres across Australia. Method 1490 young people (12–25 years) who presented to one of 11 headspace services from four geographical locations (urban New South Wales, urban South Australia, regional New South Wales, and regional Queensland) completed a digital multidimensional assessment at initial presentation. Characteristics were compared between services and geographical locations. Results We identified major variation in the demographics, and the type and severity of needs across different services. Individuals from regional services were more likely to be younger, of Aboriginal and Torres Strait Islander origin, and present with psychotic-like symptoms and suicidality, while those in urban areas were more likely to have previously sought help and have problematic alcohol use. Further differences in age, distress, depressive symptoms, psychotic-like experiences, trauma, family history, alcohol use, education/employment engagement, and days out of role were identified between different urban sites. Conclusions The variability between services provides insight into the heterogeneity of youth mental health populations which has implications for appropriate early intervention and prevention service provisions. We propose that integrating digital technologies has the potential to provide insights for smarter service planning and evaluation.