Where are all the males? Gender-specific typologies of childhood adversity based on a large community sample

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Background: Methodological issues have been identified when quantifying exposure to adversity and abuse. To address a single type may obscure covarying effects. To sum multiple exposures gives equal weight to each. Latent class analysis (LCA) addresses this by identifying homogenous subpopulations. Most studies using LCA have pooled gender data in spite of evidence that the nature and frequency of exposure differs by gender. Males report more interpersonal abuse, females report more of other exposures, particularly sexual.
Objective: This study aimed to identify if stratifying data by gender resulted in different profiles of adversity/abuse.
Participants and setting: The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) wave II, a large community-based survey, nationally representative of the US population.
Method: This study used 14 indicators of childhood adversity as the basis for LCA.
Results: The number and nature of classes differed by gender. The best solution for females was 4-class: a low risk class; a class at higher risk of sexual abuse; a class at higher risk of physical abuse; a class at higher risk of combined physical and sexual abuse. The best solution for males had only 3-classes; a low risk class, a class at higher risk of sexual abuse; a class at higher risk of physical abuse. The combined dataset resulted in a solution similar to the female solution.
Conclusion: The importance of developing models for males and females separately was evidenced by the male and female classes being differentially associated with mental health variables.
Original languageEnglish
Pages (from-to)149-159
Number of pages11
JournalChild Abuse & Neglect
Volume90
Early online date16 Feb 2019
DOIs
Publication statusPublished - 1 Apr 2019

Keywords

  • Abuse
  • Adversity
  • Childhood trauma
  • Gender
  • Latent class analysis

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