Objective: Psychiatric multimorbidity is a well-documented risk factor for suicide. However, diagnostic heterogeneity and patterns of comorbidity likely exists within the population of those who attempt suicide. Person-centered statistical approaches, such as latent class analysis (LCA), extract distinguishable groups differentiated by prevalence and comorbidity of psychiatric disorders.
Method: The present study used LCA to identify typologies of psychiatric heterogeneity in a sample of 213 inpatients (M age = 33.04 [SD = 12.67]; 57.3% female; 62.4% White; 23.9% Hispanic/Latino) with a history of suicide attempt who were recruited for a suicide prevention clinical trial. Class differences in suicide history characteristics; demographic characteristics; and cognitive-affective and behavioral risk factors, obtained from an initial evaluation involving the administration of a semi-structured diagnostic interview, suicide risk assessment, and battery of self-report measures, were explored.
Results: LCA identified three classes in the best-fitting solution: Depressive-High Comorbidity (n = 68), Depressive-Low Comorbidity (n = 86), and Bipolar (n = 59). The Depressive-Low Comorbidity class reported less severe suicidal ideation (p < .001), anxiety (p < .001), stress (p < .001), unlovability beliefs (p = .006), and impulsivity (p < .001). The Depressive-Low Comorbidity class also reported fewer actual attempts than the Bipolar class (p = .001) and fewer interrupted attempts than the Depressive-High Comorbidity class (p = .004).
Conclusions: The Depressive-High Comorbidity and Bipolar classes consistently endorsed higher levels of suicide risk factors. These findings may help to illuminate typologies of suicide attempters with unique clinical needs, which is an essential step toward personalized medicine.
Keywords: Comorbidity; risk factors; suicide prevention.
LCA identified three classes of heterogeneity in inpatients with suicide attemptsThose with more complex diagnostic profiles reported higher levels of suicide riskFindings have important implications for individualized risk interventions.