Login / Signup

Distinct profiles of violence victimization and suicide risk: Findings from a national survey of emerging adults.

Lisa FedinaCheryl KingJordan DeVylderTodd I Herrenkohl
Published in: The American journal of orthopsychiatry (2023)
Victims of bullying, dating violence, and child maltreatment are all more likely than their peers to contemplate and attempt suicide in adolescence and young adulthood. However, knowledge of the relationship between violence and suicide risk is primarily limited to studies that isolate certain forms of victimization or examine several forms in additive risk models. We aim to move beyond the findings of basic descriptive studies by investigating whether multiple types of victimization elevate risk for suicide and whether latent profiles of victimization are more strongly related to suicide-related outcomes than are others. Primary data are from the first National Survey on Polyvictimization and Suicide Risk, a cross-sectional, nationally representative survey of emerging adults 18-29 in the United States ( N = 1,077). A total of 50.2% of participants identified as cisgender female, followed by 47.4% cisgender male, and 2.3% transgender or nonbinary. Latent class analysis (LCA) was used to establish profiles. Suicide-related variables were regressed onto victimization profiles. A four-class solution was determined to be the best fitting model: Interpersonal Violence (IV; 22%), Interpersonal + Structural Violence (I + STV; 7%), Emotional Victimization (EV; 28%), and Low/No Victimization (LV; 43%). Participants in I + STV had increased odds for high suicide risk (odds ratio = 42.05, 95% CI [15.45, 114.42]) compared to those in LV, followed by IV (odds ratio = 8.52, 95% CI [3.47, 20.94]) and EV (odds ratio = 5.17, 95% CI [2.08, 12.87]). Participants in I + STV reported significantly higher odds for nonsuicidal self-injury and suicide attempts compared to most classes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Keyphrases
  • intimate partner violence
  • mental health
  • healthcare
  • emergency department
  • machine learning
  • depressive symptoms
  • big data
  • high school
  • skeletal muscle
  • weight loss
  • hiv infected
  • data analysis