1. Youth at-risk for serious mental illness: methods of the PROCAN study.
- Author
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Addington J, Goldstein BI, Wang JL, Kennedy SH, Bray S, Lebel C, Hassel S, Marshall C, and MacQueen G
- Subjects
- Adolescent, Adult, Anxiety diagnostic imaging, Anxiety epidemiology, Anxiety psychology, Bipolar Disorder diagnostic imaging, Bipolar Disorder epidemiology, Bipolar Disorder psychology, Canada epidemiology, Child, Depression diagnostic imaging, Depression epidemiology, Depression psychology, Female, Humans, Longitudinal Studies, Male, Mental Disorders epidemiology, Psychotic Disorders diagnostic imaging, Psychotic Disorders epidemiology, Psychotic Disorders psychology, Risk Factors, Young Adult, Algorithms, Family psychology, Mental Disorders diagnostic imaging, Mental Disorders psychology, Severity of Illness Index
- Abstract
Background: Most mental disorders begin in adolescence; however, there are gaps in our understanding of youth mental health. Clinical and policy gaps arise from our current inability to predict, from amongst all youth who experience mild behavioural disturbances, who will go on to develop a mental illness, what that illness will be, and what can be done to change its course and prevent its worsening to a serious mental illness (SMI). There are also gaps in our understanding of how known risk factors set off neurobiological changes that may play a role in determining who will develop a SMI. Project goals are (i) to identify youth at different stages of risk of SMI so that intervention can begin as soon as possible and (ii) to understand the triggers of these mental illnesses., Method: This 2-site longitudinal study will recruit 240 youth, ages 12-25, who are at different stages of risk for developing a SMI. The sample includes (a) healthy individuals, (b) symptom-free individuals who have a first-degree relative with a SMI, (c) youth who are experiencing distress and may have mild symptoms of anxiety or depression, and (d) youth who are already demonstrating attenuated symptoms of SMI such as bipolar disorder or psychosis. We will assess, every 6 months for one year, a wide range of clinical and psychosocial factors to determine which factors can be used to predict key outcomes. We will also assess neuroimaging and peripheral markers. We will develop and validate a prediction algorithm that includes demographic, clinical and psychosocial predictors. We will also determine if adding biological markers to our algorithm improves prediction., Discussion: Outcomes from this study include an improved clinical staging model for SMI and prediction algorithms that can be used by health care providers as decision-support tools in their practices. Secondly, we may have a greater understanding of clinical, social and cognitive factors associated with the clinical stages of development of a SMI, as well as new insights from neuroimaging and later neurochemical biomarker studies regarding predisposition to SMI development and progression through the clinical stages of illness.
- Published
- 2018
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