Evidence map›Paper›PMID 36264613›Full record

ArticleJMIR formative research2022

Mental Illness Concordance Between Hospital Clinical Records and Mentions in Domestic Violence Police Narratives: Data Linkage Study.

George Karystianis, Rina Carines Cabral, Armita Adily, Wilson Lukmanjaya, Peter Schofield, Iain Buchan, Goran Nenadic, Tony Butler

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.6field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 2 countries.

George KarystianisSchool of Population Health, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0003-3491-361X
Rina Carines CabralSchool of Population Health, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0003-3076-0521
Armita AdilySchool of Population Health, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0003-0722-3293
Wilson LukmanjayaSchool of Computer Science, University of Technology, Sydney, Australia.ORCID https://orcid.org/0000-0002-7747-4648
Peter SchofieldHunter Medical Research Institute, Newcastle, Australia.ORCID https://orcid.org/0000-0003-2967-9662
Iain BuchanInstitute of Population Health, University of Liverpool, Liverpool, United Kingdom.ORCID https://orcid.org/0000-0003-3392-1650
Goran NenadicSchool of Computer Science, University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0003-0795-5363
Tony ButlerSchool of Population Health, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0002-2679-2769
UNSW Sydney · AUHunter Medical Research Institute · AUUniversity of Liverpool · GBUniversity of Manchester · GBUniversity of Technology Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo 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.

objectiveThis 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.

methodsWe 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.

resultsUsing 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.

conclusionsOur 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.

Indexed as

data linkagedomestic violencehospital recordsmental healthpolice recordstext mining

Identifiers

PMID36264613
PMCPMC9634517
OpenAlexW4295254760

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.