The role of substance use and morality in violent crime - a qualitative study among imprisoned individuals in opioid maintenance treatment

Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data

BACKGROUND

Children 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.

METHODS

Psychiatric 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 and 1993. We trained (a) a cross-sectional random forest (RF) model using data available by age 17 to predict SUD diagnosis between ages 18 and 19; and (b) a longitudinal recurrent neural network (RNN) model with the Long Short-Term Memory (LSTM) architecture to predict new diagnoses at each age.

RESULTS

The area under the receiver operating characteristic curve (AUC) was 0.73(95%CI 0.70-0.76) for the random forest model (RF). Removing prior diagnosis from the predictors, the RF model was still able to achieve significant AUCs when predicting all SUD diagnoses (0.69, 95%CI 0.66-0.72) or new diagnoses (0.67, 95%CI: 0.64, 0.71) during age 18-19. For the model predicting new diagnoses, model calibration was good with a low Brier score of 0.086. Longitudinal LSTM model was able to predict later SUD risks at as early as 2 years age, 10 years before the earliest diagnosis. The average AUC from longitudinal models predicting new diagnoses 1, 2, 5 and 10 years in the future was 0.63.

CONCLUSIONS

Population registry data can be used to predict at-risk comorbid SUDs in individuals with ADHD. Such predictions can be made many years prior to age of the onset, and their SUD risks can be monitored using longitudinal models over years during child development. Nevertheless, 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.

An exploratory study on violence among the college students in Urban Bengaluru, Karnataka, India

Violence often blights people's lives for decades, leading to drug addiction, depression, suicide, school dropout, unemployment, and recurrent relationship difficulties. This exploratory study was conducted among professional degree colleges in urban Bengaluru from January to March 2017; the objectives were to find the magnitude, forms, and substance use in violence. A total of 450 students were studied. One hundred and twenty-three (27.3%) students experienced some kind of violence in the past 1 year and 77 (62.6%) were males. Verbal abuse (65.0%), pushing around (23.6%), and slapping (18.7%) were common forms of violence observed. Substance use was significantly associated with violence.

Drug Abuse and Behavioral Transgressions during Methadone Maintenance Treatment (MMT) are Related to High Psychopathy Levels

Introduction: Studies which used the Psychopathy Checklist-Revised (PCL-R) among methadone maintenance treatment (MMT) patients focused mostly on methodological issues, without addressing its relationship to patients' misconduct during treatment. This paper tests the hypothesis that high PCL-R scores are related to high rates of drug abuse, and high numbers of behavioral transgressions in MMT during a 7-year period. Material and Methods: 107 MMT patients were recruited from a MMT clinic in Israel, and were administered the PCL-R. The questionnaires results as well as routine drug test findings were recorded between 7/2007 and 11/2007. Seven years later (7/2014), repeated drug test results were analyzed, and the number of behavioral transgressions during the entire period was computed. Results: High levels of psychopathy were related to drug test results indicating any illicit drug use, cocaine use and benzodiazepines misuse at the beginning of study, and limited to benzodiazepines misuse among patients who stayed in treatment at the 7-year follow-up. However, higher scores on different PCL-R facets were significantly associated with different types of drugs. The PCL-R's total score and all but the antisociality facet were positively correlated with a higher number of behavioral transgressions (such as, threats and/or verbal and physical aggression). Conclusions: Administration of the PCL-R during MMT may help identify patients with high illicit drug use levels and a higher chance of committing behavioral transgressions during treatment.

[Prevalence and predictors of psychoactive substance use among men in prisons]

OBJECTIVE

The use of psychoactive substances among the prison population is an important public health issue because of its magnitude and health consequences. The aim of this study was to estimate the prevalence of psychoactive substance use among the prison population and to analyse its association with sociodemographic and penitentiary factors, particularly the size of the prison.

METHODS

Data were gathered using a self-administered questionnaire among 2,484 random male inmates in eight prisons of different sizes in Spain. The prevalence of psychoactive substance use with 95% confidence intervals (95% CI) during the last 6 months in prison were estimated. Prevalence ratios were used to estimate the association between psychoactive substance use and sociodemographic and penitentiary characteristics.

RESULTS

The prevalence of psychoactive substance use in the past 6 months in prison was 59.9% (95% CI: 57.9-62.0). Notable among the sociodemographic variables associated with substance use were drug consumption prior to imprisonment (6.90; 95% CI: 5.51-8.65) and recidivism in prison (2.41; 95% CI: 2.04-2.85). The largest prisons showed a higher frequency of drug use than other prisons.

CONCLUSIONS

A high prevalence of psychoactive substance use was found in prisons and significant differences were found according to delinquent profile and the size of the prison.