Psychiatric disorders and reoffending risk in individuals with community sentences in Sweden: a national cohort study

Background: Community sentences are widely used in many countries, often comprising the majority of criminal justice sanctions. Psychiatric disorders are highly prevalent in community-sentenced populations and are thus potential targets for treatment interventions designed to reduce reoffending. We examined the association between psychiatric disorders and reoffending in a national cohort of individuals given community sentences in Sweden, with use of a sibling control design to account for unmeasured familial confounding. Methods: We did a longitudinal cohort study of 82 415 individuals given community sentences between Nov 1, 1991, and Dec 31, 2013, in Sweden using data from population-based registers. We calculated hazard ratios (HRs) for any reoffending and violent reoffending with Cox regression models. We compared community-sentenced siblings with and without psychiatric disorders to control for potential familial confounding. Additionally, we calculated population attributable fractions to assess the contribution of psychiatric disorders to reoffending behaviours. The primary outcomes of the study were any (general) reoffending and violent reoffending. Findings: Between Nov 1, 1991, and Dec 31, 2013, those given community sentences who were diagnosed with any psychiatric disorder had an increased reoffending risk in men (adjusted HR 1·59, 95% CI 1·56–1·63 for any reoffending; 1·60, 1·54–1·66 for violent reoffending) and women (1·71, 1·61–1·82 for any reoffending; 2·19, 1·88–2·54 for violent reoffending). Risk estimates remained elevated after adjustment for familial confounding. Schizophrenia spectrum disorders, personality disorders, and substance use disorders had stronger associations with violent reoffending than did other psychiatric disorders. Assuming causality, the adjusted population attributable risk of psychiatric disorders on violent reoffending was 8·3% (95% CI 6·6–10·0) in the first 2 years of community follow-up in men and 30·9% (22·7–39·0) in women. Interpretation: Psychiatric disorders were associated with an increased risk of any reoffending and violent reoffending in the community-sentenced population. The magnitude of the association between psychiatric disorders and reoffending varied by individual diagnosis. Substance use disorders had the highest absolute and relative risks. Most of the increased risk for any reoffending in individuals with psychiatric disorders could be attributed to comorbid substance misuse. Given their high prevalence, substance use disorders should be the focus of treatment programmes in community-sentenced populations

Recidivism rates in individuals receiving community sentences:A systematic review

ObjectiveWe aimed to systematically review recidivism rates in individuals given community sentences internationally. We sought to explore sources of variation between these rates and how reporting practices may limit their comparability across jurisdictions. Finally, we aimed to adapt previously published guidelines on recidivism reporting to include community sentenced populations.MethodsWe searched MEDLINE, PsycINFO, SAGE and Google Scholar for reports and studies of recidivism rates using non-specific and targeted searches for the 20 countries with the largest prison populations worldwide. We identified 28 studies with data from 19 countries. Of the 20 countries with the largest prison populations, only 2 reported recidivism rates for individuals given community sentences.ResultsThe most commonly reported recidivism information between countries was for 2-year reconviction, which ranged widely from 14% to 43% in men, and 9% to 35% in women. Explanations for recidivism rate variations between countries include when the follow-up period started and whether technical violations were taken into account.ConclusionRecidivism rates in individuals receiving community sentences are typically lower in comparison to those reported in released prisoners, although these two populations differ in terms of their baseline characteristics. Direct comparisons of the recidivism rates in community sentenced cohorts across jurisdictions are currently not possible, but simple changes to existing reporting practices can facilitate these. We propose recommendations to improve reporting practices

Association of substance use and other psychiatric disorders with all-cause and external-cause mortality in individuals given community sentences in Sweden: a national cohort study

Background Consistently high rates of premature mortality have been reported in individuals who receive community sentences. However, few studies have explored potential modifiable risk factors for these rates, particularly mental health. We examined the association of substance use and other psychiatric disorders with all-cause and external-cause mortality in individuals convicted of a criminal offence and given a community sentence. Methods We did a longitudinal cohort study of 109,751 individuals given community sentences in Sweden using population-based registers. We calculated mortality rates for all-cause and external-cause mortality, hazard ratios for the association between psychiatric disorders and mortality, and population attributable fractions to quantify the contribution of psychiatric disorders to mortality risk. Findings During the follow-up, 5749 (5.2%) individuals died, including 2709 (2.5%) from external causes. Individuals with pre-existing substance use and other psychiatric disorders had an increased mortality risk from any cause (aHR = 2.28 [95% CI 2.15–2.42]) and from external causes (3.11 [2.85–3.40]) compared to individuals without known psychiatric or substance use disorders. Suicide was the most common cause of death in younger persons. Interpretation In individuals given community sentences, substance use and other psychiatric disorders were associated with an increased risk of premature death with suicide being the leading cause of death. Community supervision represents an opportunity to provide sentenced individuals with access to evidence-based treatment targeting substance misuse and psychiatric disorders to prevent potentially preventable deaths. Funding Wellcome Trust

Bootstrapping the P300 in diagnostic psychophysiology:How many iterations are needed?

In psychophysiological research, bootstrapping procedures are often used to classify individual participants. How many iterations are required for a reliable bootstrap test is not universally agreed upon. To investigate the number of iterations needed for a stable bootstrap estimate, we reanalyzed P300 data collected in concealed information test paradigms. We also distinguished between the bootstrap and permutations approaches. We compared results in several studies using 100 versus 1,000 versus 10,000 iterations in the bootstrap, and we concluded that 100 iterations were adequate as results from all three iteration numbers correlated highly.</p

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented