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Mental Illness Concordance Between Hospital Clinical Records and Mentions in Domestic Violence Police Narratives: Data Linkage Study.
Karystianis, George; Cabral, Rina Carines; Adily, Armita; Lukmanjaya, Wilson; Schofield, Peter; Buchan, Iain; Nenadic, Goran; Butler, Tony.
Afiliação
  • Karystianis G; School of Population Health, University of New South Wales, Sydney, Australia.
  • Cabral RC; School of Population Health, University of New South Wales, Sydney, Australia.
  • Adily A; School of Population Health, University of New South Wales, Sydney, Australia.
  • Lukmanjaya W; School of Computer Science, University of Technology, Sydney, Australia.
  • Schofield P; Hunter Medical Research Institute, Newcastle, Australia.
  • Buchan I; Institute of Population Health, University of Liverpool, Liverpool, United Kingdom.
  • Nenadic G; School of Computer Science, University of Manchester, Manchester, United Kingdom.
  • Butler T; School of Population Health, University of New South Wales, Sydney, Australia.
JMIR Form Res ; 6(10): e39373, 2022 Oct 20.
Article em En | MEDLINE | ID: mdl-36264613
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: JMIR Form Res Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Austrália

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: JMIR Form Res Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Austrália