Trajectories of Dynamic Risk Factors as Predictors of Violence and Criminality in Patients Discharged From Mental Health Services

A Longitudinal Study Using Growth Mixture Modeling

Bibliographic Data

ID15529262
AuthorsMélissa Beaudoin (0000-0002-0169-8055, Institut Universitaire en Santé Mentale de Québec, corresponding author), Stéphane Potvin (0000-0003-1624-378X, Université de Montréal), Laura Dellazizzo (0000-0001-8262-130X, Université de Montréal), Mimosa Luigi (0000-0001-9862-2411, Université de Montréal), Charles‐Édouard Giguère (0000-0002-8507-6412, Institut Universitaire en Santé Mentale de Québec), Charles-Édouard Giguère, Alexandre Dumais (0000-0002-4480-0064, Université de Montréal)
Year2019
Volume10
Pages301-301
Publication date2019-05-09
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2019.00301
PMID31139099
OpenAlexW2944796473
LanguageEN
Citations received4
References cited57

Background: Individuals with severe mental illnesses are at greater risk of offenses and violence, though the relationship remains unclear due to the interplay of static and dynamic risk factors. Static factors have generally been emphasized, leaving little room for temporal changes in risk. Hence, this longitudinal study aims to identify subgroups of psychiatric populations at risk of violence and criminality by taking into account the dynamic changes of symptomatology and substance use. Method: A total of 825 patients from the MacArthur Violence Risk Assessment Study having completed five postdischarge follow-ups were analyzed. Individuals were classified into outcome trajectories (violence and criminality). Trajectories were computed for each substance (cannabis, alcohol, and cocaine, alone or combined) and for symptomatology and inputted as dynamic factors, along with other demographic and psychiatric static factors, into binary logistic regressions for predicting violence and criminality. Best predictors were then identified using backward elimination, and receiver operator characteristic (ROC) curves were calculated for both models. Results: Two trajectories were found for violence (low versus high violence). Best predictors for belonging in the high-violence group were low verbal intelligence (baseline), higher psychopathy (baseline) and anger (mean) scores, persistent cannabis use (alone), and persistent moderate affective symptoms. The model's area under the curve (AUC) was 0.773. Two trajectories were also chosen as being optimal for criminality. The final model to predict high criminality yielded an AUC of 0.788, retaining as predictors male sex, lower educational level, higher score of psychopathy (baseline), persistent polysubstance use (cannabis, cocaine, and alcohol), and persistent cannabis use (alone). Both models were moderately predictive of outcomes. Conclusion: Static factors identified as predictors are consistent with previously published literature. Concerning dynamic factors, unexpectedly, cannabis alone was an independent co-occurring variable, as well as affective symptoms, in the violence model. For criminality, our results are novel, as there are very few studies on criminal behaviors in nonforensic psychiatric populations. In conclusion, these results emphasize the need to further study the predictors of crime, separately from violence and the impact of longitudinal patterns of specific substance use and high affective symptoms

Cannabis · Injury prevention · Logistic regression · Longitudinal study · Medical emergency · Mental health · Personality · Poison control · Polysubstance dependence · Psychiatry · Psychopathy · Substance abuse · Clinical Psychology · Crime Patterns and Interventions · Medicine · Personality Disorders and Psychopathology · Psychology · Psychopathy, Forensic Psychiatry, Sexual Offending · Internal Medicine

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