Individuals at high risk for suicide are categorically distinct from those at low risk.
Although suicide risk is often thought of as existing on a graded continuum, its latent structure (i.e., whether it is categorical or dimensional) has not been empirically determined. Knowledge about the latent structure of suicide risk holds implications for suicide risk assessments, targeted suicide interventions, and suicide research. Our objectives were to determine whether suicide risk can best be understood as a categorical (i.e., taxonic) or dimensional entity, and to validate the nature of any obtained taxon. We conducted taxometric analyses of cross-sectional, baseline data from 16 independent studies funded by the Military Suicide Research Consortium. Participants (N = 1,773) primarily consisted of military personnel, and most had a history of suicidal behavior. The Comparison Curve Fit Index values for MAMBAC (.85), MAXEIG (.77), and L-Mode (.62) all strongly supported categorical (i.e., taxonic) structure for suicide risk. Follow-up analyses comparing the taxon and complement groups revealed substantially larger effect sizes for the variables most conceptually similar to suicide risk compared with variables indicating general distress. Pending replication and establishment of the predictive validity of the taxon, our results suggest the need for a fundamental shift in suicide risk assessment, treatment, and research. Specifically, suicide risk assessments could be shortened without sacrificing validity, the most potent suicide interventions could be allocated to individuals in the high-risk group, and research should generally be conducted on individuals in the high-risk group. (PsycINFO Database Record
Department of Psychology, Auburn University.
Tracy K
TK
Witte
Department of Psychology, University of Denver.
Jill M
JM
Holm-Denoma
Department of Psychology, Auburn University.
Kelly L
0000-0002-4798-898X
KL
Zuromski
Department of Psychology, Auburn University.
Jami M
JM
Gauthier
Department of Psychology, The College of New Jersey.
John
J
Ruscio
D000328:Adult; D003430:Cross-Sectional Studies; D006801:Humans; D008889:Military Personnel / Q000706:statistics & numerical data*; D018570:Risk Assessment / Q000379:methods*; D013405:Suicide / Q000706:statistics & numerical data*