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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dc.contributor.author Reis, Ben Y.
dc.contributor.author Kohane, Isaac Samuel
dc.contributor.author Mandl, Kenneth David
dc.date.accessioned 2011-03-16T14:39:20Z
dc.date.issued 2009
dc.identifier.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. en_US
dc.identifier.issn 0959-8138 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4742728
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher BMJ Publishing Group Ltd. en_US
dc.relation.isversionof doi:10.1136/bmj.b3677 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2755036/pdf/ en_US
dash.license LAA
dc.subject domestic violence en_US
dc.subject screening (epidemiology) en_US
dc.subject information management en_US
dc.subject abuse (child, partner, elder) en_US
dc.subject screening (public health) en_US
dc.subject violence against women en_US
dc.subject violence (other) en_US
dc.title Longitudinal Histories as Predictors of Future Diagnoses of Domestic Abuse: Modelling Study en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal BMJ : British Medical Journal en_US
dash.depositing.author Reis, Ben Y.
dc.date.available 2011-03-16T14:39:20Z
dash.affiliation.other HMS^Health Sciences and Technology en_US
dash.affiliation.other HMS^Pediatrics-Children's Hospital en_US
dash.affiliation.other HMS^Countway Library of Medicine en_US
dash.affiliation.other HMS^Medicine-Brigham and Women's Hospital en_US
dash.affiliation.other HMS^Pediatrics-Children's Hospital en_US
dash.affiliation.other HMS^Health Sciences and Technology en_US
dash.affiliation.other HMS^Pediatrics-Children's Hospital en_US

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