Youth at-risk for serious mental illness: methods of the PROCAN study

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dc.contributor.authorAddington, Jean
dc.contributor.authorGoldstein, Benjamin I
dc.contributor.authorWang, Jian L
dc.contributor.authorKennedy, Sidney H
dc.contributor.authorBray, Signe
dc.contributor.authorLebel, Catherine
dc.contributor.authorHassel, Stefanie
dc.contributor.authorMarshall, Catherine
dc.contributor.authorMacQueen, Glenda
dc.date.accessioned2018-09-26T12:05:17Z
dc.date.available2018-09-26T12:05:17Z
dc.date.issued2018-07-05
dc.date.updated2018-09-26T12:05:17Z
dc.description.abstractAbstract 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.
dc.identifier.citationBMC Psychiatry. 2018 Jul 05;18(1):219
dc.identifier.doihttps://doi.org/10.1186/s12888-018-1801-0
dc.identifier.urihttp://hdl.handle.net/1880/107941
dc.identifier.urihttps://doi.org/10.11575/PRISM/44503
dc.language.rfc3066en
dc.rights.holderThe Author(s).
dc.titleYouth at-risk for serious mental illness: methods of the PROCAN study
dc.typeJournal Article
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