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Longitudinal histories as predictors of future diagnoses of domestic abuse: modelling study

BMJ 2009; 339 doi: (Published 30 September 2009) Cite this as: BMJ 2009;339:b3677
  1. Ben Y Reis, assistant professor12,
  2. Isaac S Kohane, professor12,
  3. Kenneth D Mandl, associate professor12
  1. 1Children’s Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Children’s Hospital Boston, Boston, MA, USA
  2. 2Harvard Medical School, Boston, MA
  1. Correspondence to: B Y Reis, 1 Autumn St, Room 540.1, Boston, MA 02115 Ben_Reis{at}
  • Accepted 26 May 2009


Objective To determine whether longitudinal data in patients’ historical records, commonly available in electronic health record systems, can be used to predict a patient’s future risk of receiving a diagnosis of domestic abuse.

Design Bayesian models, known as intelligent histories, used to predict a patient’s risk of receiving a future diagnosis of abuse, based on the patient’s diagnostic history. Retrospective evaluation of the model’s predictions using an independent testing set.

Setting A state-wide claims database covering six years of inpatient admissions to hospital, admissions for observation, and encounters in emergency departments.

Population All patients aged over 18 who had at least four years between their earliest and latest visits recorded in the database (561 216 patients).

Main outcome measures Timeliness of detection, sensitivity, specificity, positive predictive values, and area under the ROC curve.

Results 1.04% (5829) of the patients met the narrow case definition for abuse, while 3.44% (19 303) met the broader case definition for abuse. The model achieved sensitive, specific (area under the ROC curve of 0.88), and early (10-30 months in advance, on average) prediction of patients’ future risk of receiving a diagnosis of abuse. Analysis of model parameters showed important differences between sexes in the risks associated with certain diagnoses.

Conclusions Commonly available longitudinal diagnostic data can be useful for predicting a patient’s future risk of receiving a diagnosis of abuse. This modelling approach could serve as the basis for an early warning system to help doctors identify high risk patients for further screening.


  • We thank Karen Olson for preparing the dataset for analysis.

  • Contributors: BYR designed the study, developed the models, analysed the results, wrote the manuscript, and is guarantor. ISK contributed to study design and writing the manuscript and advised on clinical issues. KDM contributed to study design and writing the manuscript and advised on clinical issues.

  • Funding: This work was supported by the US Centers for Disease Control and Prevention (grant R01 PH000040) and the National Library of Medicine (grants R01 LM009879, R01 LM007677, and G08LM009778). The funders have no involvement with the research.

  • Statement of independence of researchers from funders: The authors and the research are completely independent of the funders.

  • Competing interests: None declared.

  • Ethical approval: This study was approved by the institutional review board approval.

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