Latent class analysis identified phenotypes in individuals with schizophrenia spectrum disorder who engage in aggressive behaviour towards others

Differentiating Between Sexual Offending and Violent Non-sexual Offending in Men With Schizophrenia Spectrum Disorders Using Machine Learning

Forensic psychiatric populations commonly contain a subset of persons with schizophrenia spectrum disorders (SSD) who have committed sex offenses. A comprehensive delineation of the features that distinguish persons with SSD who have committed sex offenses from persons with SSD who have committed violent non-sex offenses could be relevant to the development of differentiated risk assessment, risk management and treatment approaches. This analysis included the patient records of 296 men with SSD convicted of at least one sex and/or violent offense who were admitted to the Centre for Inpatient Forensic Therapy at the University Hospital of Psychiatry Zurich between 1982 and 2016. Using supervised machine learning, data on 461 variables retrospectively collected from the records were compared with respect to their relative importance in differentiating between men who had committed sex offenses and men who had committed violent non-sex offenses. The final machine learning model was able to differentiate between the two types of offenders with a balanced accuracy of 71.5% (95% CI = [60.7, 82.1]) and an AUC of .80 (95% CI = [.67, .93]). The main distinguishing features included sexual behaviours and interests, psychopathological symptoms and characteristics of the index offense. Results suggest that when assessing and treating persons with SSD who have committed sex offenses, it appears to be relevant to not only address the core symptoms of the disorder, but to also take into account general risk factors for sexual recidivism, such as atypical sexual interests and sexual preoccupation.

Distinct impulsivity profiles in subtypes of violence among community-dwelling patients with severe mental disorders: a longitudinal study

BACKGROUND

Although only a few patients with severe mental disorders (SMD) can commit violent behaviour in the community, violent behaviour aggravates the stigma towards patients with SMD. Understanding the subtypes of violent behaviour may be beneficial for preventing violent behaviour among patients with SMD, but it has rarely been studied.

METHODS

This longitudinal study investigated 1914 patients with SMD in the community at baseline, and the follow-up period ranged from February 2021 to August 2021. The Barratt Impulsiveness Scale Version-11, the Buss-Perry Aggression Questionnaire, the Impulsive/Premeditated Aggression Scale, the Personality Diagnostic Questionnaire and the MacArthur Community Violence Instrument were used at baseline. The Modified Overt Aggression Scale was used to assess the occurrence of violent behaviour (outcome) during the follow-up period. Cox regression models were used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs). Latent class analysis was used to characterise the subtypes of patients with SMD who engaged in violent behaviour at follow-up.

RESULTS

We found that 7.2% of patients with SMD presented violent behaviour within six months in the community. Younger age (OR = 0.98, 95% CI = 0.96-1.00, p = 0.016) and no economic source (OR = 1.60, 95% CI = 1.10-2.33, p = 0.014) were risk factors for violent behaviour. Patients with SMD who engaged in violent behaviour could be classified into three subtypes: one class characterised by a history of violence and impulsivity, another class characterised by high levels of aggression and motor impulsivity, and the last class characterised by median cognitive impulsivity.

CONCLUSIONS

Socio-demographic factors were risk factors for violent behaviour among patients with SMD, which could eliminate the discrimination toward this group. Impulsivity played a vital role in identifying the three subtypes of patients with SMD who engaged in violent behaviour. These findings may be helpful for the development of a personalised violence risk management plan for patients with SMD who commit violent behaviour in the community.

Elusive cases in forensic psychiatry? Exploring subgroups of schizophrenia spectrum disorder patients in Germany

BACKGROUND

The relationship between schizophrenia spectrum disorders (SSD) and criminal behaviour is a central issue in forensic psychiatry. People with mental illness face some of the same types of criminogenic factors as people without mental illness, albeit more frequently. The research question of this study is the extent to which a framework of early and late offender typology can be empirically reconstructed in a forensic psychiatric population, and whether there are any practical implications.

METHOD

For N = 733 patients in six different forensic hospitals in Germany, the age at first psychiatric admission and the age at first registered offence were documented, as well as a number of other patient-related characteristics. Two clustering procedures were used to investigate whether forensic psychiatric patients could be classified according to these characteristics.

RESULTS

A k-means cluster analysis using age at first psychiatric admission, age at first recorded offence, sociodemographic, clinical and criminological characteristics supported a 4-cluster solution. MANOVA analyses revealed further differences between the identified types.

CONCLUSION

This study empirically confirms some of the sub-groups of the early and late starter typology described in the literature. In particular, the "early starters", "late starters" and "first presenters" were identified, but cluster four comprises individuals not previously described in the scientific literature. Each of these classes has group-specific characteristics that may have implications for forensic treatment, post-release aftercare, and the legal system.

Prolactin and morning cortisol concentrations in antipsychotic naïve first episode psychosis: A systematic review and meta-analysis

IMPORTANCE

Alterations in prolactin and cortisol levels have been reported in antipsychotic naïve patients with first episode psychosis (FEP). However, it has been studied in very small samples, and inter-group variability has never been studied before.

OBJECTIVE

To provide estimates of standardized mean differences (SMD) and inter-group variability for prolactin, cortisol awakening response (CAR) and morning cortisol concentrations in antipsychotic naïve FEP (AN-FEP) patients and healthy controls (HC).

DATA SOURCES

BIOSIS, KCI, MEDLINE, Russian Science Citation Index, SciELO, Cochrane, PsycINFO, Web of Science were searched from inception to February 28, 2022.

STUDY SELECTION

Peer-reviewed cohort studies that reported on prolactin or cortisol blood concentrations in AN- FEP patients and HC were included.

DATA EXTRACTION AND SYNTHESIS

Study characteristics, means and standard deviations (SD) were extracted from each article. Inter group differences in magnitude of effect were estimated using Hedges g. Inter-group variability was estimated with the coefficient of variation ratio (CVR). In both cases estimates were pooled using random-effects meta-analysis. Differences by study-level characteristics were estimated using meta-regression. PRISMA guideline was followed (No. CRD42022303555).

MAIN OUTCOMES AND MEASURES

Prolactin, CAR and morning cortisol blood concentrations in AN-FEP group in relation to HC group.

RESULTS

Fourteen studies for prolactin (N = 761 for AN-FEP group, N = 687 for HC group) and twelve studies for morning cortisol (N = 434 for AN-FEP group, N = 528 for HC group) were included. No studies were found in CAR in AN-FEP patients. Mean SMD for prolactin blood concentration was 0.88 (95% CI 0.57, 1.20) for male and 0.56 (95% CI 0.26, 0.87) for female. As a group, AN-FEP presented greater inter-group variability for prolactin levels than HC (CVR=1.28, 95% CI 1.02, 1.62). SMD for morning cortisol concentrations was non-significant: 0.34 (95% CI -0.01, 0.69) and no inter-group variability significant differences were detected: CVR= 1.05 (95% CI 0.91, 1.20). Meta-regression analyses for age and quality were non-significant. Funnel plots did not suggest a publication bias.

CONCLUSIONS AND RELEVANCE

Increased prolactin levels were found in AN-FEP patients. A greater inter-group variability in the AN-FEP group suggests the existence of patient subgroups with different prolactin levels. No significant abnormalities were found in morning cortisol levels. Further research is needed to clarify whether prolactin concentrations could be used as an illness biomarker.

Self-Harm Among Forensic Psychiatric Inpatients With Schizophrenia Spectrum Disorders: An Explorative Analysis

The burden of self-injury among offenders undergoing inpatient treatment in forensic psychiatry is substantial. This exploratory study aims to add to the previously sparse literature on the correlates of self-injury in inpatient forensic patients with schizophrenia spectrum disorders (SSD). Employing a sample of 356 inpatients with SSD treated in a Swiss forensic psychiatry hospital, patient data on 512 potential predictor variables were retrospectively collected via file analysis. The dataset was examined using supervised machine learning to distinguish between patients who had engaged in self-injurious behavior during forensic hospitalization and those who had not. Based on a combination of ten variables, including psychiatric history, criminal history, psychopathology, and pharmacotherapy, the final machine learning model was able to discriminate between self-injury and no self-injury with a balanced accuracy of 68% and a predictive power of AUC = 71%. Results suggest that forensic psychiatric patients with SSD who self-injured were younger both at the time of onset and at the time of first entry into the federal criminal record. They exhibited more severe psychopathological symptoms at the time of admission, including higher levels of depression and anxiety and greater difficulty with abstract reasoning. Of all the predictors identified, symptoms of depression and anxiety may be the most promising treatment targets for the prevention of self-injury in inpatient forensic patients with SSD due to their modifiability and should be further substantiated in future studies.

High Risk, High Dose?-Pharmacotherapeutic Prescription Patterns of Offender and Non-Offender Patients with Schizophrenia Spectrum Disorder

Compared to acute or community settings, forensic psychiatric settings, in general, have been reported to make greater use of antipsychotic polypharmacy and/or high dose pharmacotherapy, including overdosing. However, there is a scarcity of research specifically on offender patients with schizophrenia spectrum disorders (SSD), although they make up a large proportion of forensic psychiatric patients. Our study, therefore, aimed at evaluating prescription patterns in offender patients compared to non-offender patients with SSD. After initial statistical analysis with null-hypothesis significance testing, we evaluated the interplay of the significant variables and ranked them in accordance with their predictive power through application of supervised machine learning algorithms. While offender patients received higher doses of antipsychotics, non-offender patients were more likely to receive polypharmacologic treatment as well as additional antidepressants and benzodiazepines. To the authors' knowledge, this is the first study to evaluate a homogenous group of offender patients with SSD in comparison to non-offender controls regarding patterns of antipsychotic and other psychopharmacologic prescription patterns.

Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD

Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcomes as central tasks, there is a need for such predictive tools, for example, to anticipate complex treatment courses and to be able to offer appropriate therapy on an individualized basis. This study aimed to develop a first basic model for the anticipation of adverse treatment courses based on prior compulsory admission and/or conviction as simple and easily objectifiable parameters in offender patients with a schizophrenia spectrum disorder (SSD). With a balanced accuracy of 67% and an AUC of 0.72, gradient boosting proved to be the optimal ML algorithm. Antisocial behavior, physical violence against staff, rule breaking, hyperactivity, delusions of grandeur, fewer feelings of guilt, the need for compulsory isolation, cannabis abuse/dependence, a higher dose of antipsychotics (measured by the olanzapine half-life) and an unfavorable legal prognosis emerged as the ten most influential variables out of a dataset with 209 parameters. Our findings could demonstrate an example of the use of ML in the development of an easy-to-use predictive model based on few objectifiable factors.

Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia

Linear statistical methods may not be suited to the understanding of psychiatric phenomena such as aggression due to their complexity and multifactorial origins. Here, the application of machine learning (ML) algorithms offers the possibility of analyzing a large number of influencing factors and their interactions. This study aimed to explore inpatient aggression in offender patients with schizophrenia spectrum disorders (SSDs) using a suitable ML model on a dataset of 370 patients. With a balanced accuracy of 77.6% and an AUC of 0.87, support vector machines (SVM) outperformed all the other ML algorithms. Negative behavior toward other patients, the breaking of ward rules, the PANSS score at admission as well as poor impulse control and impulsivity emerged as the most predictive variables in distinguishing aggressive from non-aggressive patients. The present study serves as an example of the practical use of ML in forensic psychiatric research regarding the complex interplay between the factors contributing to aggressive behavior in SSD. Through its application, it could be shown that mental illness and the antisocial behavior associated with it outweighed other predictors. The fact that SSD is also highly associated with antisocial behavior emphasizes the importance of early detection and sufficient treatment.

Maintaining social capital in offenders with schizophrenia spectrum disorder-An explorative analysis of influential factors

The importance of "social capital" in offender rehabilitation has been well established: Stable family and community relationships offer practical assistance in the resettlement process after being released from custody and can serve as motivation for building a new sense of self off the criminal past, thus reducing the risk of re-offending. This also applies to offenders with severe mental disorders. The aim of this study was to identify factors that promote or hinder the establishment or maintenance of social relationships upon release from a court-ordered inpatient treatment using a modern statistical method-machine learning (ML)-on a dataset of 369 offenders with schizophrenia spectrum disorder (SSD). With an AUC of 0.73, support vector machines (SVM) outperformed all the other ML algorithms. The following factors were identified as most important for the outcome in respect of a successful re-integration into society: Social integration and living situation prior to the hospitalization, a low risk of re-offending at time of discharge from the institution, insight in the wrongfulness of the offense as well as into the underlying psychiatric illness and need for treatment, addressing future perspectives in psychotherapy, the improvement of antisocial behavior during treatment as well as a detention period of less than 1 year emerged as the most predictive out of over 500 variables in distinguishing patients who had a social network after discharge from those who did not. Surprisingly, neither severity and type of offense nor severity of the psychiatric illness proved to affect whether the patient had social contacts upon discharge or not. The fact that the majority of determinants which promote the maintenance of social contacts can be influenced by therapeutic interventions emphasizes the importance of the rehabilitative approach in forensic-psychiatric therapy.

Individuals with schizophrenia who act violently towards others profit unequally from inpatient treatment-Identifying subgroups by latent class analysis

BACKGROUND

People with schizophrenia show a higher risk of committing violent offenses. Previous studies indicate that there are at least three subtypes of offenders with schizophrenia.

OBJECTIVES

Employing latent class analysis, the goals of this study were to investigate the presence of homogeneous subgroups of offender patients in terms of remission in psychopathology during inpatient treatment and whether or not these are related to subtypes found in previous studies. Results should help identify patient subgroups benefitting insufficiently from forensic inpatient treatment and allow hypotheses on possibly more suitable therapy option for these patients.

METHODS

A series of latent class analyses was used to explore extensive and detailed psychopathological reports of 370 offender patients with schizophrenia before and after inpatient treatment.

RESULTS

A framework developed by Hodgins to identify subgroups of offenders suffering from schizophrenia is useful in predicting remission of psychopathology over psychiatric inpatient treatment. While "early starters" were most likely to experience remission of psychopathology over treatment, "late late starters" and a subgroup including patients from all three of Hodgins' subgroups in equal proportions benefited least. Negative symptoms generally seemed least likely to remit.

CONCLUSION

Psychiatric treatment may have to be more tailored to offender patient subgroups to allow them to benefit more equally.

Different needs in patients with schizophrenia spectrum disorders who behave aggressively towards others depend on gender: a latent class analysis approach

BACKGROUND

There is limited research with inconsistent findings on differences between female and male offender patients with a schizophrenia spectrum disorder (SSD), who behave aggressively towards others. This study aimed to analyse inhomogeneities in the dataset and to explore, if gender can account for those.

METHODS

Latent class analysis was used to analyse a mixed forensic dataset consisting of 31 female and 329 male offender patients with SSD, who were accused or convicted of a criminal offence and were admitted to forensic psychiatric inpatient treatment between 1982 and 2016 in Switzerland.

RESULTS

Two homogenous subgroups were identified among SSD symptoms and offence characteristics in forensic SSD patients that can be attributed to gender. Despite an overall less severe criminal and medical history, the female-dominated class was more likely to receive longer prison terms, similarly high antipsychotic dosages, and was less likely to benefit from inpatient treatment. Earlier findings were confirmed and extended in terms of socio-demographic variables, diseases and criminal history, comorbidities (including substance use), the types of offences committed in the past and as index offence, accountability assumed in court, punishment adjudicated, antipsychotic treatment received, and the development of symptoms during psychiatric inpatient treatment.

CONCLUSIONS

Female offender patients with schizophrenia might need a more tailored approach in prevention, assessment and treatment to diminish tendencies of inequity shown in this study.

Escape and absconding among offenders with schizophrenia spectrum disorder - an explorative analysis of characteristics

BACKGROUND

Escape and absconding, especially in forensic settings, can have serious consequences for patients, staff and institutions. Several characteristics of affected patients could be identified so far, albeit based on heterogeneous patient populations, a limited number of possible factors and basal statistical analyses. The aim of this study was to determine the most important characteristics among a large number of possible variables and to describe the best statistical model using machine learning in a homogeneous group of offender patients with schizophrenia spectrum disorder.

METHODS

A database of 370 offender patients suffering from schizophrenia spectrum disorder and 507 possible predictor variables was explored by machine learning. To counteract overfitting, the database was divided into training and validation set and a nested validation procedure was used on the training set. The best model was tested on the validation set and the most important variables were extracted.

RESULTS

The final model resulted in a balanced accuracy of 71.1% (95% CI = [58.5, 83.1]) and an AUC of 0.75 (95% CI = [0.63, 0.87]). The variables identified as relevant and related to absconding/ escape listed from most important to least important were: more frequent forbidden intake of drugs during current hospitalization, more index offences, higher neuroleptic medication, more frequent rule breaking behavior during current hospitalization, higher PANSS Score at discharge, lower age at admission, more frequent dissocial behavior during current hospitalization, shorter time spent in current hospitalization and higher PANSS Score at admission.

CONCLUSIONS

For the first time a detailed statistical model could be built for this topic. The results indicate the presence of a particularly problematic subgroup within the group of offenders with schizophrenic spectrum disorder who also tend to escape or abscond. Early identification and tailored treatment of these patients could be of clinical benefit.

Childhood Maltreatment, Psychopathology, and Offending Behavior in Patients With Schizophrenia: A Latent Class Analysis Evidencing Disparities in Inpatient Treatment Outcome

Background: Extant research has provided evidence for disparities between patients with schizophrenia spectrum disorder (SSD) who have and have not experienced childhood maltreatment (CM) in terms of treatment outcome, psychopathology and their propensity to engage in offending behavior. However, research addressing all phenomena is scarce. Objective: The current study aims to explore differences between offender patients with SSD and CM and those with SSD and no CM in terms of their offending, psychopathology at different points in time and treatment outcome. Method: In the present explorative study, latent class analysis was used to analyze differences between 197 offender patients with SSD and CM and 173 offender patients with SSD and no CM, who were admitted to forensic psychiatric inpatient treatment between 1982 and 2016 in Switzerland. Results: Three distinct homogenous classes of patients were identified, two of which were probable to have experienced significant CM. One third of patients with SSD and CM were probable to benefit from inpatient treatment, even surpassing results observable in the group without CM, whereas the other group with SSD and CM was probable to benefit less. Patients with SSD and no CM displayed more psychopathology at first diagnosis and prior to their index offense. Interclass differences in offending behavior were minimal. Conclusions: Offender patients with SSD and CM differ not only from offender patients with SSD and no CM, but also amongst themselves. While some with SSD and CM experience a remission in psychopathology and improve their prognosis for future offending behavior, others do not. Directions for future research on SSD and CM are discussed.

Patterns of Lifetime Criminality in Mentally Disordered Offenders - Findings From a Nationally Representative Cohort

Background: Treatment of mentally disordered offenders (MDOs) is challenging as their behavior and clinical conditions can be traced to a complex constellation of major mental disorders, substance use and antisocial lifestyle. Finding subgroups of these offenders, which could guide treatment and risk assessment, is desirable. There are few long-term, prospective studies of risk factors for persistent criminal behavior among MDOs. Aims: The aims are (1) to provide a map of lifetime criminality in MDOs, (2) to identify subgroups of offenders, and (3), if such clusters exist, to test whether they differ in lifetime criminality and patterns of negative events during in-patient treatment. Methods: Background data on all offenders from the Malmö University Hospital catchment area sentenced to forensic psychiatric in-patient treatment 1999-2005 (n = 125) was collected. Data on negative events during treatment (violence, threats, absconding and substance use) from date of admittance until discharge or until June 30, 2008 was gathered. Court decisions for 118 of the cohort-individuals were collected from the 1^st of January 1973 until December 31, 2013. We used hierarchical cluster analysis to identify subgroups and MANOVA-analysis to examine differences between these clusters on lifetime criminality variables and negative events. A MANCOVA was used to control for time in treatment. Results: The cohort was sentenced to a total of 3,380 crimes (944 violent) during the study period. Median age at first crime was 20 years (range 15-72), and at first violent crime 27 years (range 15-72). A subgroup (n = 26) was characterized by childhood adversities, neurodevelopmental disorders and later substance use disorders and was more often associated with substance-related crimes, financial crimes and lower age at first crime. During treatment, this cluster showed higher rates of substance use and threats. When controlling for treatment time, no differences in negative events were found. Conclusions: This study replicated findings from prison populations of the existence of a more criminally persistent phenotype characterized by early-onset neurodevelopmental and behavior disorders, childhood adversities and later substance use disorders. We did not find this cluster of variables to be related to negative events during inpatient treatment when controlling for length of stay.

Arsonists Suffering From Schizophrenia-A Description in Comparison with Other Offenders with a Similar Diagnosis

This study aims to describe the small and distinct subgroup of arsonists diagnosed with schizophrenia, their motives, personal, and crime scene characteristics. While prior research identified significant differences to other criminals, firesetters in general, or mentally disordered offenders, there are no comparisons with other offender patients with schizophrenia so far. In a forensic institution in Switzerland, a group of 30 arsonists with schizophrenia spectrum disorder (SSD) was compared to 340 other offender patients with SSD using retrograde file analysis and multiple adapted Fisher´s exact tests. While symptoms of SSD were most defining of both groups, arsonists with SSD were more often single, unemployed, prescribed psychiatric medication at index offense, had a smaller variety of criminal motives, and acted out of anger or revenge in the context of a relationship. In conclusion, symptoms of SSD may be more defining and useful in guiding clinical practice than aspects specific to arsonists.