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BMJ. 2009 Sep 29;339:b3677. doi: 10.1136/bmj.b3677.

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

Author information

1
Children's Hospital Informatics Program at the Harvard-MIT, Division of Health Sciences and Technology, Children's Hospital Boston, Boston, MA, USA. Ben_Reis@harvard.edu

Abstract

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.

PMID:
19789406
PMCID:
PMC2755036
[Indexed for MEDLINE]
Free PMC Article

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