TY - JOUR
T1 - Mental Illness Concordance Between Hospital Clinical Records and Mentions in Domestic Violence Police Narratives: Data Linkage Study
AU - Karystianis, George
AU - Cabral, Rina Carines
AU - Adily, Armita
AU - Lukmanjaya, Wilson
AU - Buchan, Iain
AU - Nenadic, Goran
AU - Butler, Tony
N1 - Publisher Copyright:
© George Karystianis, Rina Carines Cabral, Armita Adily, Wilson Lukmanjaya, Peter Schofield, Iain Buchan, Goran Nenadic, Tony Butler.
©George Karystianis, Rina Carines Cabral, Armita Adily, Wilson Lukmanjaya, Peter Schofield, Iain Buchan, Goran Nenadic, Tony Butler. Originally published in JMIR Formative Research (https://formative.jmir.org), 20.10.2022.
PY - 2022/10/20
Y1 - 2022/10/20
N2 - Background: To better understand domestic violence, data sources from multiple sectors such as police, justice, health, and welfare are needed. Linking police data to data collections from other agencies could provide unique insights and promote an all-of-government response to domestic violence. The New South Wales Police Force attends domestic violence events and records information in the form of both structured data and a free-text narrative, with the latter shown to be a rich source of information on the mental health status of persons of interest (POIs) and victims, abuse types, and sustained injuries. Objective: This study aims to examine the concordance (ie, matching) between mental illness mentions extracted from the police’s event narratives and mental health diagnoses from hospital and emergency department records. Methods: We applied a rule-based text mining method on 416,441 domestic violence police event narratives between December 2005 and January 2016 to identify mental illness mentions for POIs and victims. Using different window periods (1, 3, 6, and 12 months) before and after a domestic violence event, we linked the extracted mental illness mentions of victims and POIs to clinical records from the Emergency Department Data Collection and the Admitted Patient Data Collection in New South Wales, Australia using a unique identifier for each individual in the same cohort. Results: Using a 2-year window period (ie, 12 months before and after the domestic violence event), less than 1% (3020/416,441, 0.73%) of events had a mental illness mention and also a corresponding hospital record. About 16% of domestic violence events for both POIs (382/2395, 15.95%) and victims (101/631, 16.01%) had an agreement between hospital records and police narrative mentions of mental illness. A total of 51,025/416,441 (12.25%) events for POIs and 14,802/416,441 (3.55%) events for victims had mental illness mentions in their narratives but no hospital record. Only 841 events for POIs and 919 events for victims had a documented hospital record within 48 hours of the domestic violence event. Conclusions: Our findings suggest that current surveillance systems used to report on domestic violence may be enhanced by accessing rich information (ie, mental illness) contained in police text narratives, made available for both POIs and victims through the application of text mining. Additional insights can be gained by linkage to other health and welfare data collections.
AB - Background: To better understand domestic violence, data sources from multiple sectors such as police, justice, health, and welfare are needed. Linking police data to data collections from other agencies could provide unique insights and promote an all-of-government response to domestic violence. The New South Wales Police Force attends domestic violence events and records information in the form of both structured data and a free-text narrative, with the latter shown to be a rich source of information on the mental health status of persons of interest (POIs) and victims, abuse types, and sustained injuries. Objective: This study aims to examine the concordance (ie, matching) between mental illness mentions extracted from the police’s event narratives and mental health diagnoses from hospital and emergency department records. Methods: We applied a rule-based text mining method on 416,441 domestic violence police event narratives between December 2005 and January 2016 to identify mental illness mentions for POIs and victims. Using different window periods (1, 3, 6, and 12 months) before and after a domestic violence event, we linked the extracted mental illness mentions of victims and POIs to clinical records from the Emergency Department Data Collection and the Admitted Patient Data Collection in New South Wales, Australia using a unique identifier for each individual in the same cohort. Results: Using a 2-year window period (ie, 12 months before and after the domestic violence event), less than 1% (3020/416,441, 0.73%) of events had a mental illness mention and also a corresponding hospital record. About 16% of domestic violence events for both POIs (382/2395, 15.95%) and victims (101/631, 16.01%) had an agreement between hospital records and police narrative mentions of mental illness. A total of 51,025/416,441 (12.25%) events for POIs and 14,802/416,441 (3.55%) events for victims had mental illness mentions in their narratives but no hospital record. Only 841 events for POIs and 919 events for victims had a documented hospital record within 48 hours of the domestic violence event. Conclusions: Our findings suggest that current surveillance systems used to report on domestic violence may be enhanced by accessing rich information (ie, mental illness) contained in police text narratives, made available for both POIs and victims through the application of text mining. Additional insights can be gained by linkage to other health and welfare data collections.
KW - data linkage
KW - domestic violence
KW - hospital records
KW - mental health
KW - police records
KW - text mining
U2 - 10.2196/39373
DO - 10.2196/39373
M3 - Article
C2 - 36264613
VL - 6
JO - JMIR Formative Research
JF - JMIR Formative Research
SN - 2561-326X
IS - 10
M1 - e39373
ER -