Predictive Validity of the e-Connect Suicide Risk Classification Algorithm in Youth on Probation.

Area of Science:

• Psychiatry
• Criminology
• Public Health

Background:

• Youth on juvenile probation exhibit disproportionately high rates of suicidal thoughts and behaviors (STBs).
• Effective screening and risk stratification are crucial for this vulnerable population.
• Existing systems require enhanced tools for accurate suicide risk assessment.

Purpose of the Study:

• To evaluate the predictive validity of a tiered suicide risk classification algorithm for forecasting STBs in juvenile probationers.
• To assess the algorithm's ability to predict ideation, planning, means gathering, and attempts over 12 months.
• To determine if the algorithm provides incremental predictive value beyond baseline STB history.

Main Methods:

• A proxy of the e-Connect risk classification algorithm, using 20 items from the Global Appraisal of Individual Needs-Initial (GAIN-I), was applied to an archival dataset.
• Logistic regression analyses were conducted on 4,344 juvenile probationers with longitudinal follow-up data.
• The study examined the association between baseline risk class and subsequent STBs over a 12-month period.

Main Results:

• The baseline risk class demonstrated significant predictive power for all four measured STBs (ideation, planning, means gathering, attempts) over 12 months.
• A dose-response relationship was observed, with more severe risk classes correlating with increased likelihood of STBs.
• The risk classification algorithm retained its predictive utility even after controlling for prior history of STBs.

Conclusions:

• The tiered risk classification algorithm exhibits strong predictive validity for STBs in youth on juvenile probation.
• The findings support the algorithm's utility as a clinical decision support tool in probation settings.
• This tool can facilitate effective triage and linkage to appropriate suicide prevention services for at-risk youth.