Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study.
domestic violence
mental illnesses
police data
text mining
trend analysis
Journal
Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882
Informations de publication
Date de publication:
24 12 2020
24 12 2020
Historique:
received:
24
08
2020
accepted:
23
11
2020
revised:
17
09
2020
entrez:
28
12
2020
pubmed:
29
12
2020
medline:
16
3
2021
Statut:
epublish
Résumé
The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim's and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes. The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events. We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events. In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%). A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.
Sections du résumé
BACKGROUND
The New South Wales Police Force (NSWPF) records details of significant numbers of domestic violence (DV) events they attend each year as both structured quantitative data and unstructured free text. Accessing information contained in the free text such as the victim's and persons of interest (POI's) mental health status could be useful in the better management of DV events attended by the police and thus improve health, justice, and social outcomes.
OBJECTIVE
The aim of this study is to present the prevalence of extracted mental illness mentions for POIs and victims in police-recorded DV events.
METHODS
We applied a knowledge-driven text mining method to recognize mental illness mentions for victims and POIs from police-recorded DV events.
RESULTS
In 416,441 police-recorded DV events with single POIs and single victims, we identified 64,587 events (15.51%) with at least one mental illness mention versus 4295 (1.03%) recorded in the structured fixed fields. Two-thirds (67,582/85,880, 78.69%) of mental illnesses were associated with POIs versus 21.30% (18,298/85,880) with victims; depression was the most common condition in both victims (2822/12,589, 22.42%) and POIs (7496/39,269, 19.01%). Mental illnesses were most common among POIs aged 0-14 years (623/1612, 38.65%) and in victims aged over 65 years (1227/22,873, 5.36%).
CONCLUSIONS
A wealth of mental illness information exists within police-recorded DV events that can be extracted using text mining. The results showed mood-related illnesses were the most common in both victims and POIs. Further investigation is required to determine the reliability of the mental illness mentions against sources of diagnostic information.
Identifiants
pubmed: 33361056
pii: v22i12e23725
doi: 10.2196/23725
pmc: PMC7790609
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e23725Informations de copyright
©George Karystianis, Annabeth Simpson, Armita Adily, Peter Schofield, David Greenberg, Handan Wand, Goran Nenadic, Tony Butler. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.12.2020.
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