- Prospective data from 60 high-risk Veterans, collected repeatedly over one year to identify predictors of suicide-related events within the subsequent 90 days.
- Comprehensive measures include question-level clinical interviews, self-report scales (BDI-II, BHS, Holmes-Rahe, ATQ-30, ACSS) and trial-level neurocognitive tasks.
- Data set includes task-level trials and latent cognitive variables from computational modelling, enabling secondary analyses to predict diverse suicide-related outcomes across time frames.
Data Brief. 2026 Jul 8;67:113068. doi: 10.1016/j.dib.2026.113068. eCollection 2026 Aug.
ABSTRACT
Data were collected prospectively from 60 Veterans at risk for suicide at multiple time points over a one year period to identify predictors of suicidal events in the next 90 days. This outcome included suicide attempts, interrupted/aborted attempts, preparatory behaviors, or suicide-related hospitalizations. Entry criteria included prior year suicidal behavior or prior week severe suicidal ideation. At each time point, participants completed neurocognitive testing and self-report measures of suicidality, and reported suicide-related events since the previous time point. Available data include suicide-related outcomes, question-level data for clinical interviews and self-report measures (including assessments of suicidal thoughts and behaviors, suicidal ideation severity, as well as the Beck Depression Inventory-II (BDI-II), Beck Hopelessness Scale (BHS), Holmes-Rahe Stress Inventory, Automatic Thoughts Questionnaire (ATQ-30), and Acquired Capacity for Suicide Scale (ACSS)), and trial-level data for neurocognitive testing (including go-no/go task, a reward- and punishment-based learning task, death/suicide version of the Implicit Association Task, a memory recognition test), as well as latent cognitive variables extracted from computational modeling of the neurocognitive tests. This data may be used in secondary analyses for identification of predictors of multiple suicide-related outcomes over different time frames.
PMID:42502628 | PMC:PMC13400775 | DOI:10.1016/j.dib.2026.113068
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