The impact of adverse childhood experiences on adult physical, mental health, and abuse behaviors: A sex-stratified nationwide latent class analysis in Japan

潜在类模型 童年不良经历 心理健康 精神科 身体虐待 儿童期虐待 心理学 医学 临床心理学 性虐待 环境卫生 自杀预防 毒物控制 数学 统计
作者
Tomoya Hirai,Kosuke Hagiwara,Chong Chen,Ryo Okubo,Fumihiro Higuchi,Toshio Matsubara,Masahito Takahashi,Shin Nakagawa,Takahiro Tabuchi
出处
期刊:Journal of Affective Disorders [Elsevier BV]
标识
DOI:10.1016/j.jad.2024.10.074
摘要

Adverse childhood experiences (ACEs) have been reported to detrimentally impact physical and mental health. While experiencing multiple ACEs is common, previous research primarily assessed ACEs by their total count, neglecting the impacts of different experience types. Furthermore, sex-based differences in ACEs and their influences remain unclear. This study employed Latent Class Analysis (LCA) to uncover patterns of ACEs with consideration for sex differences, aiming to elucidate their effects on adult physical and mental health. A geographically nationally representative dataset from the "Japan COVID-19 and Society Internet Study (JACSIS)" conducted in 2022 was used. 13,715 men and 14,327 women retrospectively reported their experiences across fifteen ACEs. The analysis revealed four distinct ACE patterns for both sexes: a Multiple Adversities class with a wide range of severe ACEs, a Psychological Abuse class experiencing emotional abuse at home and bullying at school, a Poverty class facing economic hardships, and a Low Adversities class with the fewest ACEs. Multinomial logistic regression analysis indicated that more severe patterns of exposure correlated with heightened adverse adult outcomes. However, the extent of these impacts varied by sex and ACE pattern. For instance, men in Multiple Adversities and Psychological Abuse classes exhibited higher tendencies towards conducting physical and psychological abuse behaviors. While ACEs in men were linked to both underweight (in cases of psychological abuse) and obesity (across all classes), women with ACEs generally leaned towards higher body weight. These findings highlight the importance of developing support strategies sensitive to sex differences and the specific content of ACEs.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
戒骄戒躁戒熬夜完成签到,获得积分10
1秒前
1秒前
wanci应助松林采纳,获得10
3秒前
3秒前
cdercder完成签到,获得积分0
3秒前
3秒前
ding应助夕未息采纳,获得10
4秒前
酷波er应助松林采纳,获得10
4秒前
刘振扬发布了新的文献求助10
4秒前
走呗完成签到 ,获得积分10
4秒前
5秒前
星辰大海应助科研通管家采纳,获得10
6秒前
小二郎应助科研通管家采纳,获得10
6秒前
6秒前
科研通AI2S应助科研通管家采纳,获得10
6秒前
科目三应助科研通管家采纳,获得10
6秒前
无极微光应助科研通管家采纳,获得20
6秒前
6秒前
华子的五A替身完成签到,获得积分10
7秒前
7秒前
7秒前
CodeCraft应助科研通管家采纳,获得10
7秒前
Derrrick发布了新的文献求助10
7秒前
7秒前
7秒前
打打应助科研通管家采纳,获得10
7秒前
7秒前
7秒前
7秒前
NexusExplorer应助想发JHM采纳,获得10
7秒前
7秒前
7秒前
7秒前
F二次方应助科研通管家采纳,获得30
7秒前
帅气若魔发布了新的文献求助10
7秒前
上官若男应助科研通管家采纳,获得10
7秒前
8秒前
科研通AI6.1应助松林采纳,获得10
8秒前
wanci应助李玉采纳,获得10
9秒前
冷傲的擎完成签到 ,获得积分10
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
On the Dragon Seas, a sailor's adventures in the far east 500
Yangtze Reminiscences. Some Notes And Recollections Of Service With The China Navigation Company Ltd., 1925-1939 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6355929
求助须知:如何正确求助?哪些是违规求助? 8170753
关于积分的说明 17202051
捐赠科研通 5411996
什么是DOI,文献DOI怎么找? 2864440
邀请新用户注册赠送积分活动 1841940
关于科研通互助平台的介绍 1690226