A predictive model to help identify intimate partner violence based on diagnoses and phone calls

Abstract

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.