Bamford Centre for Mental Health and Wellbeing, Ulster University, Northern Ireland, United Kingdom.
School of Psychology, Ulster University, Northern Ireland, United Kingdom.
Child Abuse Negl. 2019 Apr;90:149-159. doi: 10.1016/j.chiabu.2019.02.006. Epub 2019 Feb 16.
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.
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.
This study used 14 indicators of childhood adversity as the basis for LCA.
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.
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.
在量化逆境和虐待暴露时,已经确定了方法学问题。仅量化一种类型可能会掩盖共同的影响。对多种暴露进行总结会给每个暴露同等的权重。潜在类别分析 (LCA) 通过识别同质亚群来解决这个问题。尽管有证据表明,暴露的性质和频率因性别而异,但大多数使用 LCA 的研究都汇集了性别数据。男性报告更多的人际虐待,女性报告更多其他暴露,尤其是性方面的。
本研究旨在确定按性别分层数据是否会导致逆境/虐待的不同模式。
全国酒精相关情况调查(NESARC)第二波,这是一项大型基于社区的调查,代表了美国的全国人口。
本研究使用 14 项儿童逆境指标作为 LCA 的基础。
类别的数量和性质因性别而异。女性的最佳解决方案是 4 类:低风险类;性虐待风险较高的类;身体虐待风险较高的类;身体和性虐待风险较高的类。男性的最佳解决方案只有 3 类:低风险类、性虐待风险较高的类、身体虐待风险较高的类。综合数据集的解决方案类似于女性的解决方案。
男性和女性模型分开开发的重要性得到了证明,因为男性和女性类别与心理健康变量的关联不同。