Longitudinal Histories as Predictors of Future Diagnoses of Domestic Abuse: Modelling Study

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Longitudinal Histories as Predictors of Future Diagnoses of Domestic Abuse: Modelling Study

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Title: Longitudinal Histories as Predictors of Future Diagnoses of Domestic Abuse: Modelling Study
Author: Reis, Ben Y.; Kohane, Isaac Samuel; Mandl, Kenneth David

Note: Order does not necessarily reflect citation order of authors.

Citation: Reis, Ben Y., Isaac S. Kohane, and Kenneth D. Mandl. 2009. Longitudinal histories as predictors of future diagnoses of domestic abuse: modelling study. BMJ: British Medical Journal 339:b3677.
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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.
Published Version: doi:10.1136/bmj.b3677
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2755036/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4742728

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