Youth Mental Health

Our major development in early intervention development is to detect and prevent at an early stage of the evolution of psychopathology, particularly for young people in whom the most onset of mental disorder occur. Designing awareness and early intervention program for young people is a global challenge. It is important to offer engaging youth-friendly programs. It is important to study and characterise early psychopathological experiences in non-clinical populations and to relate symptoms to risk factors. Young people in the at-risk state can be increasingly reliably identified with increasingly refined technology (Further refinement of at-risk prediction is a key focus in research). Recent data suggest that the evolution of such at risk-states can be captured with changing patterns in a number of symptom dimensions.  At  an

early stage, symptoms occur in different dimensions and are less severe. At a later stage, symptoms become more specifically focused on specific dimensions. Preventative intervention can then be offered to reduce the progression to a more definitive expression of an established illness.

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Our well-established work in early detection (public awareness and open referral system) and phase-specific intervention using case management for psychotic disorders provide an excellent base for this development. Our previous studies have established the efficacy of this system in improving outcome. Further enhancements could be achieved through effective relapse monitoring and prevention, as well as enhancement of cognitive function through physical exercise. Both of these components are being developed with clinical studies, as well as the application of digital technology. The treatment refractory state can be minimised through better pharmacological strategies, such as the prediction of clozapine response, as well as the use of long-acting depot medication. This cohort can be consolidated with a comprehensive digital system whereby more refined course and outcome information can be fed into a naturalistic data pool with the possibility of using deep-learning to identify prognostic factors at each stage of the illness (e.g. treatment response, remission, relapse, treatment-refractory, ultra-refractory).

Long-term outcome data is key for informing many clinical decisions. We have successfully established a number of long-term cohorts either from naturalistic studies or randomized controlled studies. The assessment of long-term outcome is particularly valuable for samples which have received an earlier RCT and subsequently received similar care. This design will importantly allow the elucidation of the consequences of treatment decision at an earlier stage in the course of the illness. For example, we have identified that early medication discontinuation is associated with a more adverse outcome 10 years later.