Depression, Anxiety, and Symptom Profiles Among Female and Male Victims of Sexual Violence

Ekta Choudhary · M. Dwayne Smith · Robert M. Bossarte

88 citations0/0 files storedAmerican Journal of Men s Health

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Sexual Assault and Victimization Studies Intimate Partner and Family Violence Child Abuse and Trauma

Abstract

Sexual violence is a serious public health problem that has been associated with negative mental and physical health outcomes. Few existing studies have examined the prevalence and patterns of adverse mental health among victims of sexual violence using data from nationally representative samples of U.S. adults. The main objectives of this study were to identify patterns in the associations between sexual violence victimization and depression and anxiety (DA) symptoms using data from the sexual violence and DA Behavioral Risk Factor Surveillance System (BRFSS) modules. Stratified multivariate logistic regression models were conducted to test the associations between sexual violence victimization and DA controlling for demographic characteristics. Multiple stratified MANOVA models were used to detect the effect of sexual violence victimization on DA symptoms while controlling for key demographic characteristics. Among all 61,187 participants, more than 5% (n = 3,240) were victims of sexual violence, out of which 18.82% reported being diagnosed with depression, 8.37% reported an anxiety disorder, and 28.28% reported being diagnosed with DA disorder. Victims of sexual violence reported significantly higher number of days when they had trouble concentrating, sleep difficulties, poor appetite, little interest or pleasure in activities, blamed themselves for personal failure, felt depressed, and had little energy. The present study highlights the importance of collecting nationally representative data from victims of sexual violence and extends previous findings from clinically based studies. This study also serves as an example of an analytic approach that addresses a public health priority area by drawing on data from multiple topic-specific BRFSS modules.

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