scholarly journals Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study

10.2196/23725 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e23725
Author(s):  
George Karystianis ◽  
Annabeth Simpson ◽  
Armita Adily ◽  
Peter Schofield ◽  
David Greenberg ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Mental Illness ◽  
Text Mining ◽  
New South ◽  
Mental Illnesses ◽  
Free Text ◽  
South Wales ◽  
Police Records ◽  
Health Justice ◽  
Improve Health

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.

scholarly journals Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study (Preprint)

2020 ◽  
Author(s):  
George Karystianis ◽  
Annabeth Simpson ◽  
Armita Adily ◽  
Peter Schofield ◽  
David Greenberg ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Mental Illness ◽  
Text Mining ◽  
New South ◽  
Mental Illnesses ◽  
Free Text ◽  
South Wales ◽  
Police Records ◽  
Health Justice ◽  
Improve Health

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.

scholarly journals Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study (Preprint)

2018 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter Schofield ◽  
Lee Knight ◽  
Clara Galdon ◽  
...  
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Text Mining ◽  
Large Scale ◽  
New South ◽  
New South Wales ◽  
Mental Health Disorders ◽  
Free Text ◽  
South Wales ◽  
Health Disorders

BACKGROUND Vast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes. OBJECTIVE In this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text. METHODS We used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims. RESULTS The precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.30%, 3258) and POIs (18.73%, 8918), followed by alcohol abuse for POIs (12.24%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.43%, 1671). CONCLUSIONS The results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.

Domestic violence events involving autism: a text mining study of police records in New South Wales, 2005-2016

2020 ◽  
Vol 78 ◽  
pp. 101634
Author(s):  
Ye In Hwang ◽  
Lidan Zheng ◽  
George Karystianis ◽  
Vicki Gibbs ◽  
Kym Sharp ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
New South ◽  
New South Wales ◽  
South Wales ◽  
Police Records

scholarly journals Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study (Preprint)

2018 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter W Schofield ◽  
David Greenberg ◽  
Louisa Jorm ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
Automated Analysis ◽  
Public Health Research ◽  
Free Text ◽  
Large Set ◽  
South Wales ◽  
Police Reports ◽  
Relationship Of ◽  
The Relationship

BACKGROUND The police attend numerous domestic violence events each year, recording details of these events as both structured (coded) data and unstructured free-text narratives. Abuse types (including physical, psychological, emotional, and financial) conducted by persons of interest (POIs) along with any injuries sustained by victims are typically recorded in long descriptive narratives. OBJECTIVE We aimed to determine if an automated text mining method could identify abuse types and any injuries sustained by domestic violence victims in narratives contained in a large police dataset from the New South Wales Police Force. METHODS We used a training set of 200 recorded domestic violence events to design a knowledge-driven approach based on syntactical patterns in the text and then applied this approach to a large set of police reports. RESULTS Testing our approach on an evaluation set of 100 domestic violence events provided precision values of 90.2% and 85.0% for abuse type and victim injuries, respectively. In a set of 492,393 domestic violence reports, we found 71.32% (351,178) of events with mentions of the abuse type(s) and more than one-third (177,117 events; 35.97%) contained victim injuries. “Emotional/verbal abuse” (33.46%; 117,488) was the most common abuse type, followed by “punching” (86,322 events; 24.58%) and “property damage” (22.27%; 78,203 events). “Bruising” was the most common form of injury sustained (51,455 events; 29.03%), with “cut/abrasion” (28.93%; 51,284 events) and “red marks/signs” (23.71%; 42,038 events) ranking second and third, respectively. CONCLUSIONS The results suggest that text mining can automatically extract information from police-recorded domestic violence events that can support further public health research into domestic violence, such as examining the relationship of abuse types with victim injuries and of gender and abuse types with risk escalation for victims of domestic violence. Potential also exists for this extracted information to be linked to information on the mental health status.

Nonfatal Strangulation During Domestic Violence Events in New South Wales: Prevalence and Characteristics Using Text Mining Study of Police Narratives

2021 ◽  
pp. 107780122110259
Author(s):  
Mandy Wilson ◽  
Erin Spike ◽  
George Karystianis ◽  
Tony Butler
Keyword(s):  
Domestic Violence ◽  
Text Mining ◽  
New South ◽  
Intimate Partner ◽  
Public Health Issue ◽  
South Wales ◽  
Police Data ◽  
Intimate Partner Relationships ◽  
Rich Information ◽  
Partner Relationships

Nonfatal strangulation (NFS) is a common form of domestic violence (DV) that frequently leaves no visible signs of injury and can be a portent for future fatality. A validated text mining approach was used to analyze a police dataset of 182,949 DV events for the presence of NFS. Results confirmed NFS within intimate partner relationships is a gendered form of violence. The presence of injury and/or other (non-NFS) forms of physical abuse, emotional/verbal/social abuse, and the perpetrator threatening to kill the victim, were associated with significantly higher odds of NFS perpetration. Police data contain rich information that can be accessed using automated methodologies such as text mining to add to our understanding of this pressing public health issue.

scholarly journals Latent profiles of early developmental vulnerabilities in a New South Wales child population at age 5 years

2017 ◽  
Vol 52 (6) ◽  
pp. 530-541 ◽  
Author(s):  
Melissa J Green ◽  
Stacy Tzoumakis ◽  
Kristin R Laurens ◽  
Kimberlie Dean ◽  
Maina Kariuki ◽  
...  
Keyword(s):  
Mental Illness ◽  
Mental Disorders ◽  
Mental Disorder ◽  
Early Development ◽  
Latent Class ◽  
New South ◽  
Criminal Offending ◽  
Parental History ◽  
South Wales ◽  
History Of

Objective: Detecting the early emergence of childhood risk for adult mental disorders may lead to interventions for reducing subsequent burden of these disorders. We set out to determine classes of children who may be at risk for later mental disorder on the basis of early patterns of development in a population cohort, and associated exposures gleaned from linked administrative records obtained within the New South Wales Child Development Study. Methods: Intergenerational records from government departments of health, education, justice and child protection were linked with the Australian Early Development Census for a state population cohort of 67,353 children approximately 5 years of age. We used binary data from 16 subdomains of the Australian Early Development Census to determine classes of children with shared patterns of Australian Early Development Census–defined vulnerability using latent class analysis. Covariates, which included demographic features (sex, socioeconomic status) and exposure to child maltreatment, parental mental illness, parental criminal offending and perinatal adversities (i.e. birth complications, smoking during pregnancy, low birth weight), were examined hierarchically within latent class analysis models. Results: Four classes were identified, reflecting putative risk states for mental disorders: (1) disrespectful and aggressive/hyperactive behaviour, labelled ‘misconduct risk’ ( N = 4368; 6.5%); (2) ‘pervasive risk’ ( N = 2668; 4.0%); (3) ‘mild generalised risk’ ( N = 7822; 11.6%); and (4) ‘no risk’ ( N = 52,495; 77.9%). The odds of membership in putative risk groups (relative to the no risk group) were greater among children from backgrounds of child maltreatment, parental history of mental illness, parental history of criminal offending, socioeconomic disadvantage and perinatal adversities, with distinguishable patterns of association for some covariates. Conclusion: Patterns of early childhood developmental vulnerabilities may provide useful indicators for particular mental disorder outcomes in later life, although their predictive utility in this respect remains to be established in longitudinal follow-up of the cohort.

The health service contact patterns of people with psychotic and non-psychotic forms of severe mental illness in New South Wales, Australia: A record-linkage study

2021 ◽  
pp. 000486742110314
Author(s):  
Rachael C Cvejic ◽  
Preeyaporn Srasuebkul ◽  
Adrian R Walker ◽  
Simone Reppermund ◽  
Julia M Lappin ◽  
...  
Keyword(s):  
Mental Health ◽  
Emergency Department ◽  
Mental Illness ◽  
Health Service ◽  
Confidence Interval ◽  
Physical Health ◽  
Severe Mental Illness ◽  
New South ◽  
Service Utilisation ◽  
South Wales

Objective: To describe and compare the health profiles and health service use of people hospitalised with severe mental illness, with and without psychotic symptoms. Methods: We conducted a historical cohort study using linked administrative datasets, including data on public hospital admissions, emergency department presentations and ambulatory mental health service contacts in New South Wales, Australia. The study cohort comprised 169,306 individuals aged 12 years and over who were hospitalised at least once with a mental health diagnosis between 1 July 2002 and 31 December 2014. Of these, 63,110 had a recorded psychotic illness and 106,196 did not. Outcome measures were rates of hospital, emergency department and mental health ambulatory service utilisation, analysed using Poisson regression. Results: People with psychotic illnesses had higher rates of hospital admission (adjusted incidence rate ratio (IRR) 1.26; 95% confidence interval [1.23, 1.30]), emergency department presentation (adjusted IRR 1.17; 95% confidence interval [1.13, 1.20]) and ambulatory mental health treatment days (adjusted IRR 2.90; 95% confidence interval [2.82, 2.98]) than people without psychotic illnesses. The higher rate of hospitalisation among people with psychotic illnesses was driven by mental health admissions; while people with psychosis had over twice the rate of mental health admissions, people with other severe mental illnesses without psychosis (e.g. mood/affective, anxiety and personality disorders) had higher rates of physical health admissions, including for circulatory, musculoskeletal, genitourinary and respiratory disorders. Factors that predicted greater health service utilisation included psychosis, intellectual disability, greater medical comorbidity and previous hospitalisation. Conclusion: Findings from this study support the need for (a) the development of processes to support the physical health of people with severe mental illness, including those without psychosis; (b) a focus in mental health policy and service provision on people with complex support needs, and (c) improved implementation and testing of integrated models of care to improve health outcomes for all people experiencing severe mental illness.

News Paper Reports of Bank Robberies and the Copycat Phenomenon

1992 ◽  
Vol 25 (1) ◽  
pp. 83-88 ◽  
Author(s):  
Ronald V Clarke ◽  
Gerry McGrath
Keyword(s):  
New South ◽  
New South Wales ◽  
Newspaper Coverage ◽  
Bank Robbery ◽  
South Wales ◽  
Police Records

In response to concern about newspaper coverage of bank robberies in Australia, this study examines whether newspaper publicity resulting from successful bank robberies leads to copycat increases of bank and other robberies during the following week. It makes use of police records of robberies in New South Wales and newspaper stories for 1987 to 1989 from the two tabloids published daily in Sydney. No evidence of any copycat effect was found. Moreover, there was little evidence that the newspapers concerned were paying undue attention to bank robbery.

scholarly journals Needs of women patients with mental illness

2001 ◽  
Vol 7 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Rosalind Ramsay ◽  
Sarah Welch ◽  
Elizabeth Youard
Keyword(s):  
Domestic Violence ◽  
Mental Illness ◽  
Sexual Abuse ◽  
Childhood Sexual Abuse ◽  
Mental Disorders ◽  
Health Professionals ◽  
Mental Illnesses ◽  
Women Patients ◽  
Specific Disorders ◽  
Gender Specific

Women patients suffer from a range of mental disorders similar to those that men may experience. However, there are some striking differences in the prevalence of specific disorders, and in their presentation and management. Some mental illnesses only occur in women. It seems that women patients may have a different experience of treatment, a consequence of differences in their needs and also of the way that health professionals perceive those needs. These differences are embedded in the wider cultural milieu in which we live. There are particular issues for women patients in relation to, for example, childhood sexual abuse, rape and domestic violence. At present, tools to measure needs of individual patients are generally not gender specific.

Reliability of Psychiatric Evidence in Serious Criminal Matters: Fitness to Stand Trial and the Defence of Mental Illness

2009 ◽  
Vol 43 (5) ◽  
pp. 446-452 ◽  
Author(s):  
Matthew Large ◽  
Olav Nielssen ◽  
Gordon Elliott
Keyword(s):  
Mental Illness ◽  
Expert Witness ◽  
New South ◽  
New South Wales ◽  
Consecutive Series ◽  
Expert Opinions ◽  
South Wales ◽  
Fitness To Stand Trial ◽  
Criminal Matters ◽  
Level Of Agreement

Objective: The criminal justice system relies on the opinions of expert witness to assist in decisions about fitness to stand trial (FST) and verdicts of not guilty by reason of mental illness (NGMI). The aim of the present study was to assess the level of agreement between experts about these legal issues using a consecutive series of serious criminal matters in New South Wales. Methods: Pairs of reports from 110 consecutive criminal matters completed by the New South Wales Office of the Director of Public Prosecutions between 2005 and 2007 were examined. The opinions of experts about FST and NGMI were recorded. Results: Agreement about FST was fair–moderate (experts engaged by opposite sides, κ = 0.293; experts engaged by the same side, κ = 0.471), although there was a higher level of agreement in homicide matters. Agreement about NGMI was moderate–good (experts engaged by opposite sides, κ = 0.508; experts engaged by the same side, κ = 0.644) and there was a higher level of agreement when the experts also agreed about the diagnosis of schizophrenia. Further analysis using generalized estimating equations did not find a higher level of agreement about FST or NGMI in pairs of reports containing the opinion of experts from the same side. Conclusions: Little evidence was found for bias in expert opinions about either FST or NGMI, but the comparatively low level of agreement about FST suggests the need for reform in the way that FST is assessed.

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