Dataset Information

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

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.

SUBMITTER: Karystianis G

PROVIDER: S-EPMC7790609 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Karystianis George G Simpson Annabeth A Adily Armita A Schofield Peter P Greenberg David D Wand Handan H Nenadic Goran G Butler Tony T

Journal of medical Internet research 20201224 12

<h4>Background</h4>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.<h4>Objective</h4>The aim o ...[more]

PMID: 33361056

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Project description: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.

Project description:Family and Domestic violence (FDV) is a global problem with significant social, economic, and health consequences for victims including increased health care costs, mental trauma, and social stigmatization. In Australia, the estimated annual cost of FDV is $22 billion, with one woman being murdered by a current or former partner every week. Despite this, tools that can predict future FDV based on the features of the person of interest (POI) and victim are lacking. The New South Wales Police Force attends thousands of FDV events each year and records details as fixed fields (e.g., demographic information for individuals involved in the event) and as text narratives which describe abuse types, victim injuries, threats, including the mental health status for POIs and victims. This information within the narratives is mostly untapped for research and reporting purposes. After applying a text mining methodology to extract information from 492,393 FDV event narratives (abuse types, victim injuries, mental illness mentions), we linked these characteristics with the respective fixed fields and with actual mental health diagnoses obtained from the NSW Ministry of Health for the same cohort to form a comprehensive FDV dataset. These data were input into five deep learning models (MLP, LSTM, Bi-LSTM, Bi-GRU, BERT) to predict three FDV offense types ("hands-on," "hands-off," "Apprehended Domestic Violence Order (ADVO) breach"). The transformer model with BERT embeddings returned the best performance (69.00% accuracy; 66.76% ROC) for "ADVO breach" in a multilabel classification setup while the binary classification setup generated similar results. "Hands-off" offenses proved the hardest offense type to predict (60.72% accuracy; 57.86% ROC using BERT) but showed potential to improve with fine-tuning of binary classification setups. "Hands-on" offenses benefitted least from the contextual information gained through BERT embeddings in which MLP with categorical embeddings outperformed it in three out of four metrics (65.95% accuracy; 78.03% F1-score; 70.00% precision). The encouraging results indicate that future FDV offenses can be predicted using deep learning on a large corpus of police and health data. Incorporating additional data sources will likely increase the performance which can assist those working on FDV and law enforcement to improve outcomes and better manage FDV events.

Project description:Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents' data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents' care linking tool. The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents' care and lives.Meaningful words were extracted through Standard Query Language (SQL) with the LIKE function and wildcards to perform pattern matching, followed by text mining and a word cloud using R® packages. Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents' sample as well as on other health data using a health model measuring the residents' care level needs.By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents' health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents' data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives.This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents' health and quality of care by adding new, simple, useable data to the electronic health record (EHR). When used with a normalized physiotherapy problem list, text mining through information extraction (IE), named entity recognition (NER) and data mining (DM) can provide a real advantage to describe health care, adding new medical material and helping to integrate the EHR system into the health staff work environment.

Project description:ImportancePolice violence is reportedly widespread in the United States and may pose a significant risk to public mental health.ObjectiveTo examine the association between 12-month exposure to police violence and concurrent mental health symptoms independent of trauma history, crime involvement, and other forms of interpersonal violence exposure.Design, setting, and participantsThis cross-sectional, general population survey study of 1221 eligible adults was conducted in Baltimore, Maryland, and New York City, New York, from October through December 2017. Participants were identified through Qualtrics panels, an internet-based survey administration service using quota sampling.ExposuresPast 12-month exposure to police violence, assessed using the Police Practices Inventory. Subtypes of violence exposure were coded according to the World Health Organization domains of violence (ie, physical, sexual, psychological, and neglectful).Main outcomes and measuresCurrent Kessler Screening Scale for Psychological Distress (K6) score, past 12-month psychotic experiences (World Health Organization Composite International Diagnostic Interview), and past 12-month suicidal ideation and attempts.ResultsOf 1221 eligible participants, there were 1000 respondents (81.9% participation rate). The sample matched the adult population of included cities on race/ethnicity (non-Hispanic white, 339 [33.9%]; non-Hispanic black/African American, 390 [39.0%]; Hispanic/Latino, 178 [17.8%]; other, 93 [9.3%]), age (mean [SD], 39.8 [15.2] years), and gender (women, 600 [60.0%]; men, 394 [39.4%]; transgender, 6 [0.6%]) within 10% above or beyond 2010 census distributions. Twelve-month prevalence of police violence was 3.2% for sexual violence, 7.5% for physical violence without a weapon, 4.6% for physical violence with a weapon, 13.2% for psychological violence, and 14.9% for neglect. Police violence exposures were higher among men, people of color, and those identified as homosexual or transgender. Respondents reported suicidal ideation (9.1%), suicide attempts (3.1%), and psychotic experiences (20.6%). The mean (SD) K6 score was 5.8 (6.1). All mental health outcomes were associated with police violence exposure in adjusted logistic regression analyses. Physical violence with a weapon and sexual violence were associated with greater odds of psychotic experiences (odds ratio [95% CI]: 4.34 [2.05-9.18] for physical violence with a weapon; 6.61 [2.52-17.36] for sexual violence), suicide attempts (odds ratio [95% CI]: 7.30 [2.94-18.14] for physical violence with a weapon; 6.63 [2.64-16.64] for sexual violence), and suicidal ideation (odds ratio [95% CI]: 2.72 [1.30-5.68] for physical violence with a weapon; 3.76 [1.72-8.20] for sexual violence).Conclusions and relevancePolice violence was commonly reported, especially among racial/ethnic and sexual minorities. Associations between violence and mental health outcomes did not appear to be explained by confounding factors and appeared to be especially pronounced for assaultive forms of violence.

Project description:BackgroundDomestic violence and abuse (DVA) are associated with increased risk of mental illness, but we know little about the mental health of female DVA survivors seeking support from domestic violence services.ObjectiveOur goal was to characterise the demography and mental health of women who access specialist DVA services in the United Kingdom and to investigate associations between severity of abuse and measures of mental health and health state utility, accounting for important confounders and moderators.DesignBaseline data on 260 women enrolled in a randomized controlled trial of a psychological intervention for DVA survivors were analysed. We report the prevalence of and associations between mental health status and severity of abuse at the time of recruitment. We used logistic and normal regression models for binary and continuous outcomes, respectively. The following mental health measures were used: Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM), Patient Health Questionnaire, Generalised Anxiety Disorder Assessment, and the Posttraumatic Diagnostic Scale to measure posttraumatic stress disorder (PTSD). The Composite Abuse Scale (CAS) measured abuse.ResultsExposure to DVA was high, with a mean CAS score of 56 (SD 34). The mean CORE-OM score was 18 (SD 8) with 76% above the clinical threshold (95% confidence interval: 70-81%). Depression and anxiety levels were high, with means close to clinical thresholds, and more than three-quarters of respondents recorded PTSD scores above the clinical threshold. Symptoms of mental illness increased stepwise with increasing severity of DVA.ConclusionsWomen DVA survivors who seek support from DVA services have recently experienced high levels of abuse, depression, anxiety, and especially PTSD. Clinicians need to be aware that patients presenting with mental health conditions or symptoms of depression or anxiety may be experiencing or have experienced DVA. The high psychological morbidity in this population means that trauma-informed psychological support is needed for survivors who seek support from DVA services.

Dataset Information

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

Background

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

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