Background: Little is known about the extent to which being a victim of domestic violence is associated with different mental disorders in men and women. We aimed to estimate the prevalence and odds of being a victim of domestic violence by diagnostic category and sex. Methods: Study design: Systematic review and meta-analysis. Data Sources: Eighteen biomedical and social sciences databases (including MEDLINE, EMBASE, PsycINFO); journal hand searches; scrutiny of references and citation tracking of included articles; expert recommendations, and an update of a systematic review on victimisation and mental disorder. Inclusion criteria: observational and intervention studies reporting prevalence or odds of being a victim of domestic violence in men and women (aged >= 16 years), using validated diagnostic measures of mental disorder. Procedure: Data were extracted and study quality independently appraised by two reviewers. Analysis: Random effects meta-analyses were used to pool estimates of prevalence and odds. Results: Forty-one studies were included. There is a higher risk of experiencing adult lifetime partner violence among women with depressive disorders (OR 2.77 (95% CI 1.96-3.92), anxiety disorders (OR 4.08 (95% CI 2.39-6.97), and PTSD (OR 7.34 95% CI 4.50-11.98), compared to women without mental disorders. Insufficient data were available to calculate pooled odds for other mental disorders, family violence (i.e. violence perpetrated by a non-partner), or violence experienced by men. Individual studies reported increased odds for women and men for all diagnostic categories, including psychoses, with a higher prevalence reported for women. Few longitudinal studies were found so the direction of causality could not be investigated. Conclusions: There is a high prevalence and increased likelihood of being a victim of domestic violence in men and women across all diagnostic categories, compared to people without disorders. Longitudinal studies are needed to identify pathways to being a victim of domestic violence to optimise healthcare responses.
