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Longitudinal risk profiling for suicidal thoughts and behaviours in a community cohort using decision trees
Philip J Batterham1, Helen Christensen
1Centre for Mental Health Research, The Australian National University, Canberra, ACT 0200, Australia. philip.batterham@anu.edu.au
Journal of Affective Disorders
|July 31, 2012
Summary
Understanding suicide risk is complex. This study found that past suicidality, anxiety, depression, neuroticism, and rumination are key predictors, but specific risk factors vary by mental health status.
Area of Science:
- Psychiatry
- Public Health
- Epidemiology
Background:
- Limited understanding of the interplay between suicide risk factors.
- Need for individualized suicide risk models.
Purpose of the Study:
- To develop individual models of suicide risk using a decision tree approach.
- To identify key risk factors for suicidality in different subpopulations.
Main Methods:
- Population cohort study of 6656 Australian adults.
- Four-year follow-up assessing mental health, physical health, personality, substance use, social support, social stressors, and background characteristics.
- Association analysis with suicidal ideation and behaviors.
Main Results:
- Previous suicidality, anxiety, depression, neuroticism, and rumination were strongest predictors.
- Divergent factors predicted suicidal thoughts and behaviors across mental health spectrums.
- Substance use specifically predicted suicidality in individuals with moderate anxiety or depression.
Conclusions:
- Decision tree approach offers a promising method for individualized suicide risk assessment.
- Tailored intervention programs can be developed based on identified risk factors.
- Further research is needed to confirm if changes in risk factors influence suicidality.
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