Prediction of violent reoffending on release from prison: derivation and external validation of a scalable tool
TL;DR: A prediction model in a Swedish prison population that can assist with decision making on release by identifying those who are at low risk of future violent offending, and those at high risk of violent reoffending who might benefit from drug and alcohol treatment is developed.
About: This article is published in The Lancet Psychiatry.The article was published on 2016-06-01 and is currently open access. It has received 68 citations till now. The article focuses on the topics: Population & Poison control.
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TL;DR: Almost a quarter of newly incarcerated prisoners of both sexes had an alcohol use disorder, and the prevalence of a drug use disorder was at least as high in men, and higher in women.
Abstract: Aims
The aims were to (1) estimate the prevalence of alcohol and drug use disorders in prisoners on reception to prison and (2) estimate and test sources of between study heterogeneity.
Methods
Studies reporting the 12-month prevalence of alcohol and drug use disorders in prisoners on reception to prison from 1 January 1966 to 11 August 2015 were identified from seven bibliographic indexes. Primary studies involving clinical interviews or validated instruments leading to DSM or ICD diagnoses were included; self-report surveys and investigations that assessed individuals more than 3 months after arrival to prison were not. Random-effects meta-analysis and subgroup and meta-regression analyses were conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed.
Results
In total, 24 studies with a total of 18 388 prisoners across 10 countries were identified. The random-effects pooled prevalence estimate of alcohol use disorder was 24% [95% confidence interval (CI) = 21–27], with very high heterogeneity (I2 = 94%). These ranged from 16 to 51% in male and 10–30% in female prisoners. For drug use disorders, there was evidence of heterogeneity by sex, and the pooled prevalence estimate in male prisoners was 30% (95% CI = 22–38; I2 = 98%; 13 studies; range 10–61%) and, in female prisoners, was 51% (95% CI = 43–58; I2 = 95%; 10 studies; range 30–69%). On meta-regression, sources of heterogeneity included higher prevalence of drug use disorders in women, increasing rates of drug use disorders in recent decades, and participation rate.
Conclusions
Substance use disorders are highly prevalent in prisoners. Approximately a quarter of newly incarcerated prisoners of both sexes had an alcohol use disorder, and the prevalence of a drug use disorder was at least as high in men, and higher in women.
310 citations
Additional excerpts
...Structured, simple and scalable tools to identify those at highest risk [70] and case management [71] may assist in this process....
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TL;DR: It is shown how TBI is a risk factor for earlier, more violent, offending and poor engagement in treatment, in-custody infractions, and reconviction.
107 citations
TL;DR: It is argued that data are needed to prevent excessive reliance on risk assessment scores, allow matching of different risk assessment tools to different contexts of application, protect against problematic forms of discrimination and stigmatisation, and ensure that contentious demographic variables are not prematurely removed from risk Assessment tools.
70 citations
Cites background from "Prediction of violent reoffending o..."
...Currently deployed tools frequently do use demographic factors such as age and immigration status as predictors, and although recent evidence suggests that including such demographic factors improves predictive accuracy [34,35], further data are needed to confirm this....
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...demographic factors included in current tools add incremental validity to tool performance [34]....
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TL;DR: Evidence is synthesized on the prevalence of mental disorders in adolescents in juvenile detention and correctional facilities and sources of heterogeneity between studies that found higher prevalences of ADHD and conduct disorder in investigations published after 2006 that should be given to reviewing whether health care services in Juvenile detention can address these levels of psychiatric morbidity.
Abstract: Objective To synthesize evidence on the prevalence of mental disorders in adolescents in juvenile detention and correctional facilities and examine sources of heterogeneity between studies. Method Electronic databases and relevant reference lists were searched to identify surveys published from January 1966 to October 2019 that reported on the prevalence of mental disorders in unselected populations of detained adolescents. Data on the prevalence of a range of mental disorders (psychotic illnesses, major depression, attention-deficit/hyperactivity disorder [ADHD], conduct disorder, and posttraumatic stress disorder [PTSD]) along with predetermined study characteristics were extracted from the eligible studies. Analyses were reported separately for male and female adolescents, and findings were synthesized using random-effects models. Potential sources of heterogeneity were examined by meta-regression and subgroup analyses. Results Forty-seven studies from 19 countries comprising 28,033 male and 4,754 female adolescents were identified. The mean age of adolescents assessed was 16 years (range, 10–19 years). In male adolescents, 2.7% (95% CI 2.0%–3.4%) had a diagnosis of psychotic illness; 10.1% (95% CI 8.1%–12.2%) major depression; 17.3% (95% CI 13.9%–20.7%) ADHD; 61.7% (95% CI 55.4%–67.9%) conduct disorder; and 8.6% (95% CI 6.4%–10.7%) PTSD. In female adolescents, 2.9% (95% CI 2.4%–3.5%) had a psychotic illness; 25.8% (95% CI 20.3%–31.3%) major depression; 17.5% (95% CI 12.1%–22.9%) ADHD; 59.0% (95% CI 44.9%–73.1%) conduct disorder; and 18.2% (95% CI 13.1%–23.2%) PTSD. Meta-regression found higher prevalences of ADHD and conduct disorder in investigations published after 2006. Female adolescents had higher prevalences of major depression and PTSD than male adolescents. Conclusion Consideration should be given to reviewing whether health care services in juvenile detention can address these levels of psychiatric morbidity.
63 citations
TL;DR: The neuropsychiatric disorders were among the strongest in relative and absolute terms and national strategies for the prevention of interpersonal violence may need to review policies concerning the identification and treatment of modifiable risk factors.
Abstract: BackgroundInterpersonal violence is a leading cause of morbidity and mortality. The strength and population effect of modifiable risk factors for interpersonal violence, and the quality of the research evidence is not known.AimsWe aimed to examine the strength and population effect of modifiable risk factors for interpersonal violence, and the quality and reproducibility of the research evidence.MethodWe conducted an umbrella review of systematic reviews and meta-analyses of risk factors for interpersonal violence. A systematic search was conducted to identify systematic reviews and meta-analyses in general population samples. Effect sizes were extracted, converted into odds ratios and synthesised, and population attributable risk fractions (PAF) were calculated. Quality analyses were performed, including of small study effects, adjustment for confounders and heterogeneity. Secondary analyses for aggression, intimate partner violence and homicide were conducted, and systematic reviews (without meta-analyses) were summarised.ResultsWe identified 22 meta-analyses reporting on risk factors for interpersonal violence. Neuropsychiatric disorders were among the strongest in relative and absolute terms. The neuropsychiatric risk factor that had the largest effect at a population level were substance use disorders, with a PAF of 14.8% (95% CI 9.0–21.6%), and the most important historical factor was witnessing or being a victim of violence in childhood (PAF = 12.2%, 95% CI 6.5–17.4%). There was evidence of small study effects and large heterogeneity.ConclusionsNational strategies for the prevention of interpersonal violence may need to review policies concerning the identification and treatment of modifiable risk factors.Declarations of interestJ.R.G. is an NIHR Senior Investigator. The views expressed within this article are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.
60 citations
References
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TL;DR: In this article, an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, which are particularly needed for binary, ordinal, and time-to-event outcomes.
Abstract: Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
7,879 citations
24 Aug 2005
TL;DR: An easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes.
Abstract: Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
4,905 citations
01 Jan 1996
TL;DR: An easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, which are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes.
Abstract: SUMMARY Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression. Accurate estimation of patient prognosis is important for many reasons. First, prognostic estimates can be used to inform the patient about likely outcomes of her disease. Second, the physician can use estimates of prognosis as a guide for ordering additional tests and selecting appropriate therapies. Third, prognostic assessments are useful in the evaluation of technologies; prognostic estimates derived both with and without using the results of a given test can be compared to measure the incremental prognostic information provided by that test over what is provided by prior information.' Fourth, a researcher may want to estimate the effect of a single factor (for example, treatment given) on prognosis in an observational study in which many uncontrolled confounding factors are also measured. Here the simultaneous effects of the uncontrolled variables must be controlled (held constant mathematically if using a regression model) so that the effect of the factor of interest can be more purely estimated. An analysis of how variables (especially continuous ones) affect the patient outcomes of interest is necessary to
4,782 citations
4,476 citations
TL;DR: In virtually all medical domains, diagnostic and prognostic multivariable prediction models are being developed, validated, updated, and implemented with the aim to assist doctors and individuals in estimating probabilities and potentially influence their decision making.
Abstract: The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
2,982 citations