Data-driven identification of subtypes of intimate partner violence.

Intimate partner violence (IPV) is a complex problem with multiple layers of heterogeneity. We took a data-driven approach to characterize this heterogeneity. We integrated data from different studies, representing 640 individuals from various backgrounds. We used hierarchical clustering to systematically group cases in terms of their similarities according to violence variables. Results suggested that the cases can be clustered into 12 hierarchically organized subgroups, with verbal abuse and negotiation being the main discriminatory factors at higher levels. The presence of physical assault, injury, and sexual coercion was discriminative at lower levels of the hierarchy. Subgroups also exhibited significant differences in terms of relationship dynamics and individual factors. This study represents an attempt toward using integrative data analysis to understand the etiology of violence. These results can be useful in informing treatment efforts. The integrative data analysis framework we develop can also be applied to various other problems.

Identifying health correlates of intimate partner violence against pregnant women.

PURPOSE: Violence against women during pregnancy is a serious public health concern due to its significant adverse health consequences for both the mother and the baby. This study aims to systematically identify common health problems and synergistic health correlates of intimate partner violence (IPV) that specifically affect pregnant women. METHODS: We mine large-scale electronic health record (EHR) data from the IBM Explorys database to identify health problems that are prevalent in both IPV and pregnancy populations, as well those that are synergistically associated with the presence of IPV during pregnancy. For this purpose, we develop methods that enhance the statistical reliability of identified patterns by constructing confidence intervals that take into account systematic bias and measurement errors in addition to the variance in estimation. RESULTS: We identify with high confidence 668 and 2750 terms that are respectively prevalent in respectively IPV and pregnancy populations. Of these terms, 279 are common. We also identify 16 synergistic health correlates with high confidence. Our results suggest that mental health, substance abuse, and genitourinary complications are prevalent among pregnant women exposed to IPV. The synergistic terms we identify reveal potential conditions that can be direct consequences of trauma (e.g., tibial fracture), long-term health consequences (e.g., chronic rhinitis), markers associated with the demograhics of affected populations (e.g., acne), and risk factors that potentially increase vulnerability during pregnancy (e.g., disorders of attention and motor control). CONCLUSIONS: Our results indicate that IPV significantly affects the well-being of pregnant women in multiple ways. The findings of this study can be useful for screening of IPV in pregnant women. Finally, the methodology presented here can also be useful for investigating the synergy between other medical conditions using EHR databases with privacy constraints.