A Predictive Model to Help Identify Intimate Partner Violence Based on Diagnoses and Phone Calls

Background Intimate partner violence (IPV) is a significant health problem but goes largely undiagnosed, undisclosed, and clinically undocumented. Purpose To use historical data on diagnoses and telephone advice calls to develop a predictive model that identifies clinical profiles of women at high risk for undisclosed IPV. Methods A case–control study was conducted in women aged 18–44 years enrolled at Kaiser Permanente Northern California (KPNC) in 2005–2006 using symptoms reported by telephone and clinical diagnosis from electronic medical records. Analysis was conducted in 2007–2010. Overall, 1276 cases were identified using ICD-9 codes for IPV and were matched with 5 controls each. A full multivariate model was developed to identify those with IPV, as well as a reduced model and a summed-score model whose performance characteristics were assessed. Results Predictors most highly associated with IPV were history of remote IPV (OR=7.8); calls or diagnoses for psychiatric problems (OR=2.4); calls for HIV concerns (OR=2.4); and clinical diagnoses of prenatal complications (OR=2.1). Using the summed-score model for a population with IPV prevalence of 7%, and using a threshold score of 3 for predicting IPV with a sensitivity of 75%, 9.7 women would need to be assessed to diagnose one case of IPV. Conclusions Diagnosed IPV was associated with a clinical profile based on both telephone call data and clinical diagnoses. The simple predictive model can prompt focused clinical inquiry and improve diagnosis of IPV in any clinical setting.