ne in four young people experience mental ill health by the age of 25. 1 As these disorders typically emerge during adolescence and early adulthood, they often have functional outcomes that extend into later life. 2 Consequently, responding early is the key to reducing their overall impact.The value of early intervention is supported by evidence that the longer the period of untreated illness, the poorer the outcomes. 3 Early intervention clinics attract young people with subthreshold or early stage disorders, 4 many of whom are already subject to substantial functional impairment, comorbidity, and suicidality. 5,6 The heterogeneity of symptoms, risk, and functioning at their first presentation means that providing timely interventions that meet all of a young person's needs can be difficult. 7 Short term reductions in psychological distress and risk are typically reported for young people who attend early intervention clinics, 8 but most will later experience deterioration of symptoms or chronic functional impairment. 5 Trajectory-based modelling takes into account the heterogeneity of young people who require mental health care by identifying subpopulations of young people, with the aim of guiding service planning and strategies for improving long term functional outcomes. 9,10 Our study evaluated trajectories of functioning during the first two years of early intervention care, and identified factors associated with these trajectories. MethodsWe identified our participants in a research registry of 6743 people aged 12-30 years who presented to the youth mental health clinics at the Brain and Mind Centre (University of Sydney) during 1 June 2008 -31 July 2018. These clinics provide both primary care services (headspace) and more specialised services.The clinics are not diagnosis-specific, do not impose symptom-, severity-, or risk-based thresholds for care, and attract young people with a broad range of emerging anxiety, depressive, mania-like, psychosis-like, and comorbid syndromes. Case management was provided for all participants by clinicians, and clients received appropriate psychological, social, and medical interventions as standard care. Those whose needs exceeded the capacity of the primary care services were referred to more specialised mental health services or were hospitalised.The inclusion criteria for participation were age 12-25 years at baseline, and at least three data points between one and 24 months after baseline. Data collectionData were extracted from clinical files to a standardised form, as previously described. 11 For each participant, their first

Background A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making regarding a service response in terms of more detailed assessments and/or intervention. The aim of this study was to predict self-harm within six-months after initial presentation. Method The study included 1962 young people (12–30 years) presenting to youth mental health services in Australia. Six machine learning algorithms were trained and tested with ten repeats of ten-fold cross-validation. The net benefit of these models were evaluated using decision curve analysis. Results Out of 1962 young people, 320 (16%) engaged in self-harm in the six months after first assessment and 1642 (84%) did not. The top 25% of young people as ranked by mean predicted probability accounted for 51.6% - 56.2% of all who engaged in self-harm. By the top 50%, this increased to 82.1%-84.4%. Models demonstrated fair overall prediction (AUROCs; 0.744–0.755) and calibration which indicates that predicted probabilities were close to the true probabilities (brier scores; 0.185–0.196). The net benefit of these models were positive and superior to the ‘treat everyone’ strategy. The strongest predictors were (in ranked order); a history of self-harm, age, social and occupational functioning, sex, bipolar disorder, psychosis-like experiences, treatment with antipsychotics, and a history of suicide ideation. Conclusion Prediction models for self-harm may have utility to identify a large sub population who would benefit from further assessment and targeted (low intensity) interventions. Such models could enhance health service approaches to identify and reduce self-harm, a considerable source of distress, morbidity, ongoing health care utilisation and mortality.

Most mental disorders emerge before the age of 25 years and, if left untreated, have the potential to lead to considerable lifetime burden of disease. Many services struggle to manage high demand and have difficulty matching individuals to timely interventions due to the heterogeneity of disorders. The technological implementation of clinical staging for youth mental health may assist the early detection and treatment of mental disorders. We describe the development of a theory-based automated protocol to facilitate the initial clinical staging process, its intended use, and strategies for protocol validation and refinement. The automated clinical staging protocol leverages the clinical validation and evidence base of the staging model to improve its standardization, scalability, and utility by deploying it using Health Information Technologies (HIT). Its use has the potential to enhance clinical decision-making and transform existing care pathways, but further validation and evaluation of the tool in real-world settings is needed.

Background eHealth tools that assess and track health outcomes in children or young people are an emerging type of technology that has the potential to reform health service delivery and facilitate integrated, interdisciplinary care. Objective The aim of this review is to summarize eHealth tools that have assessed and tracked health in children or young people to provide greater clarity around the populations and settings in which they have been used, characteristics of digital devices (eg, health domains, respondents, presence of tracking, and connection to care), primary outcomes, and risks and challenges of implementation. Methods A search was conducted in PsycINFO, PubMed or MEDLINE, and Embase in April 2020. Studies were included if they evaluated a digital device whose primary purpose was to assess and track health, focused on children or young people (birth to the age of 24 years), reported original research, and were published in peer-reviewed journals in English. Results A total of 39 papers were included in this review. The sample sizes ranged from 7 to 149,329 participants (median 163, mean 5155). More studies were conducted in urban (18/39, 46%) regions than in rural (3/39, 8%) regions or a combination of urban and rural areas (8/39, 21%). Devices were implemented in three main settings: outpatient health clinics (12/39, 31%), hospitals (14/39, 36%), community outreach (10/39, 26%), or a combination of these settings (3/39, 8%). Mental and general health were the most common health domains assessed, with a single study assessing multiple health domains. Just under half of the devices tracked children’s health over time (16/39, 41%), and two-thirds (25/39, 64%) connected children or young people to clinical care. It was more common for information to be collected from a single informant (ie, the child or young person, trained health worker, clinician, and parent or caregiver) than from multiple informants. The health of children or young people was assessed as a primary or secondary outcome in 36% (14/39) of studies; however, only 3% (1/39) of studies assessed whether using the digital tool improved the health of users. Most papers reported early phase research (formative or process evaluations), with fewer outcome evaluations and only 3 randomized controlled trials. Identified challenges or risks were related to accessibility, clinical utility and safety, uptake, data quality, user interface or design aspects of the device, language proficiency or literacy, sociocultural barriers, and privacy or confidentiality concerns; ways to address these barriers were not thoroughly explored. Conclusions eHealth tools that assess and track health in children or young people have the potential to enhance health service delivery; however, a strong evidence base validating the clinical utility, efficacy, and safety of tools is lacking, and more thorough investigation is needed to address the risks and challenges of using these emerging technologies in clinical care. At present, there is greater potential for the tools to facilitate multi-informant, multidomain assessments and longitudinally track health over time and room for further implementation in rural or remote regions and community settings around the world.

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