Criminal recidivism in offenders with personality disorders and substance use disorders over 8years of time at risk

2011 ◽  
Vol 186 (2-3) ◽  
pp. 443-445 ◽  
Author(s):  
Marc Walter ◽  
Gerhard A. Wiesbeck ◽  
Volker Dittmann ◽  
Marc Graf
Keyword(s):  
At Risk ◽  
Substance Use ◽  
Personality Disorders ◽  
Substance Use Disorders ◽  
Criminal Recidivism

scholarly journals Can population registry data predict which children with ADHD are at risk of later substance use disorders?

2020 ◽  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Child Psychiatry ◽  
Registry Data ◽  
Early Prediction ◽  
Children With Adhd ◽  
Youth With Adhd

The first study to examine the potential of machine learning in early prediction of later substance use disorders (SUDs) in youth with ADHD has been published in the Journal of Child Psychiatry and Psychology.

Substance Use Disorders, Personality Disorders, and Eating Disorders

2016 ◽  
pp. 711-714
Author(s):  
Brian A. Palmer
Keyword(s):  
Eating Disorders ◽  
Substance Use ◽  
Personality Disorders ◽  
Substance Use Disorders ◽  
Prescription Drugs ◽  
Psychotic Symptoms ◽  
Drug Induced ◽  
Medical Settings ◽  
Generic Term ◽  
And Behavior

Psychosis is a generic term used to describe altered thought and behavior in which the patient is incapable of interpreting his or her situation rationally and accurately. Psychotic symptoms can occur in various medical, neurologic, and psychiatric disorders. Many psychotic reactions seen in medical settings are associated with the use of recreational or prescription drugs. Some of these drug-induced psychotic reactions are nearly indistinguishable from schizophrenia in terms of hallucinations and paranoid delusions.

Co-Occurrence of Personality Disorders with Mood, Anxiety, and Substance use Disorders in a Young Adult Population

2006 ◽  
Vol 20 (1) ◽  
pp. 102-112 ◽  
Author(s):  
Liisa Kantojärvi ◽  
Juha Veijola ◽  
Kristian Läksy ◽  
Jari Jokelainen ◽  
Anne Herva ◽  
...  
Keyword(s):  
Substance Use ◽  
Young Adult ◽  
Personality Disorders ◽  
Substance Use Disorders ◽  
Adult Population ◽  
Young Adult Population

scholarly journals Prevalence, Comorbidity, and Sociodemographic Correlates of Psychiatric Disorders in the All Of Us Biobank

2021 ◽  
Author(s):  
Peter B. Barr ◽  
Tim B. Bigdeli ◽  
Jacquelyn M. Meyers
Keyword(s):  
Substance Use ◽  
Anxiety Disorders ◽  
Personality Disorders ◽  
Psychiatric Disorders ◽  
Substance Use Disorders ◽  
Low Socioeconomic Status ◽  
Large Scale ◽  
Demographic Correlates ◽  
Sociodemographic Correlates ◽  
Demographic Covariates

ABSTRACTImportanceAll of Us is a landmark initiative for population-scale research into the etiology of psychiatric disorders and disparities across various sociodemographic categories.ObjectiveTo estimate the prevalence, comorbidity, and demographic covariates of psychiatric and substance use disorders in the All of Us biobank.Design, Setting, and ParticipantsWe estimated prevalence, overlap, and demographic correlates for psychiatric disorders derived from electronic health records in the All of Us biobank (release 5; N = 331,380)ExposuresSocial and demographic covariates.Main Outcome and MeasuresPsychiatric disorders derived from ICD10CM codes and grouped into phecodes across six broad domains: mood disorders, anxiety disorders, substance use disorders, stress-related disorders, schizophrenia, and personality disorders.ResultsThe prevalence of various disorders ranges from approximately 15% to less than 1%, with mood and anxiety disorders being the most common, followed by substance use disorders, stress-related disorders, schizophrenia, and personality disorders. There is substantial overlap among disorders, with a large portion of those with a disorder (~57%) having two or more registered diagnoses and tetrachoric correlations ranging from 0.43 – 0.74. The prevalence of disorders across demographic categories demonstrates that non-Hispanic whites, those of low socioeconomic status, women and those assigned female at birth, and sexual minorities are at greatest risk for most disorders.Conclusions and RelevanceAlthough the rates of disorders in All of Us are lower than rates for disorders in the general population, there is considerable variation, comorbidity, and differences across social groups. Large-scale resources like All of Us will prove to be invaluable for understanding the causes and consequences of psychiatric conditions. As we move towards an era of precision medicine, we must work to ensure it is delivered in an equitable manner.

scholarly journals Machine-Learning Prediction of Comorbid Substance Use Disorders in ADHD Youth Using Swedish Registry Data

2019 ◽  
Author(s):  
Yanli Zhang-James ◽  
Qi Chen ◽  
Ralf Kuja-Halkola ◽  
Paul Lichtenstein ◽  
Henrik Larsson ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Registry Data ◽  
Longitudinal Models ◽  
Cross Sectional ◽  
Using Data

AbstractBackgroundChildren with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.MethodsPsychiatric and somatic diagnoses, family history of these disorders, measures of socioeconomic distress and information about birth complications were obtained from the national registers in Sweden for 19,787 children with ADHD born between 1989-1993. We trained 1) cross-sectional machine learning models using data available by age 17 to predict SUD diagnosis between ages 18-19; and 2) a longitudinal model to predict new diagnoses at each age.ResultsThe area under the receiver operating characteristic curve (AUC) was 0.73 and 0.71 for the random forest and multilayer perceptron cross-sectional models. A prior diagnosis of SUD was the most important predictor, accounting for 25% of correct predictions. However, after excluding this predictor, our model still significantly predicted the first-time diagnosis of SUD during age 18-19 with an AUC of 0.67. The average of the AUCs from longitudinal models predicting new diagnoses one, two, five and ten years in the future was 0.63.ConclusionsSignificant predictions of at-risk co-morbid SUDs in individuals with ADHD can be achieved using population registry data, even many years prior to the first diagnosis. Longitudinal models can potentially monitor their risks over time. More work is needed to create prediction models based on electronic health records or linked population-registers that are sufficiently accurate for use in the clinic.

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