Citation

Karystianis G, Simpson A, Adily A, Schofield P, Greenberg D, Wand H, Nenadic G, Butler T. J. Med. Internet. Res. 2020; 22(12): e23725.

Copyright

(Copyright © 2020, Centre for Global eHealth Innovation)

DOI

10.2196/23725

PMID

unavailable

Abstract

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.

Language: en

Keywords

text mining; domestic violence; trend analysis; mental illnesses; police data