title: 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.
  
  
  OBJECTIVE
  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.
authors.affiliation: Kaiser Permanente Northern California, Santa Clara, California 95051, USA. Reena.Bhargava@kp.org
authors.forename: Reena
authors.initials: R
authors.lastname: Bhargava
authors.forename: Tanya L
authors.initials: TL
authors.lastname: Temkin
authors.forename: Bruce H
authors.initials: BH
authors.lastname: Fireman
authors.forename: Abigail
authors.initials: A
authors.lastname: Eaton
authors.forename: Brigid R
authors.initials: BR
authors.lastname: McCaw
authors.forename: Krista J
authors.initials: KJ
authors.lastname: Kotz
authors.forename: Debbie
authors.initials: D
authors.lastname: Amaral
mesh_terms: D000293:Adolescent; D000328:Adult; D002140:California; D016022:Case-Control Studies; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008962:Models, Theoretical; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013179:Spouse Abuse / Q000175:diagnosis*; D013689:Telephone; D055815:Young Adult