Dynamic prediction of reoffending in individuals given community sentences: Development and validation of a novel risk monitoring assessment tool (oxMore).

IF 3.2 2区社会学 Q1 LAW

Law and Human Behavior Pub Date : 2025-12-04 DOI:10.1037/lhb0000641

Denis Yukhnenko,Nigel Blackwood,Paul Lichtenstein,Seena Fazel

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引用次数: 0

Abstract

OBJECTIVE This study aimed to develop and validate a dynamic risk assessment tool for individuals serving community sentences that accounts for the effects of acute adverse events and desistance from crime. HYPOTHESES Dynamic risk prediction models that incorporate updated data on mental health relapses, incidents of victimization, and desistance from crime will produce more accurate risk stratification for reoffending than models lacking dynamic measurement of risk factors. METHOD We analyzed a national cohort of 59,676 individuals given community sentences in Sweden, of whom 23,879 (45%) had prior psychiatric diagnoses and 18,546 (31%) had substance use disorder diagnoses. Model development tested prespecified criminal history, sociodemographic, and clinical risk factors. Employing landmarking methods for time-to-event data, we modeled the effects of new health care episodes during community supervision, changes in a supervised individual's circumstances, and the impact of crime desistance. We validated the model in a geographically distinct population. RESULTS During follow up, 18,307 (31%) were reconvicted, 4,416 (7%) committed a violent offense, and 5,381 (9%) were hospitalized with a psychiatric diagnosis. The model demonstrated strong calibration and discrimination performance (c-index = 0.74 for violent reoffending, c-index = 0.69 for general reoffending). It also outperformed comparison models that did not incorporate dynamic data. The final model was translated into an online risk calculator (OxMore). CONCLUSIONS Implementation of dynamic models could lead to more accurate risk stratification for individuals under community supervision, including those with psychiatric and substance use disorders, potentially improving resource allocation, and linkage to interventions that reduce recidivism rates. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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动态预测在社区服刑的个人再犯：开发和验证一种新的风险监测评估工具。

目的：本研究旨在开发和验证一种动态风险评估工具，用于社区服刑人员，该工具可以解释急性不良事件和犯罪消退的影响。假设与缺乏风险因素动态测量的模型相比，包含心理健康复发、受害事件和犯罪停止的最新数据的动态风险预测模型将产生更准确的再犯风险分层。方法：我们分析了瑞典59,676名社区服刑者的国家队列，其中23,879人（45%）有精神疾病诊断，18,546人（31%）有物质使用障碍诊断。模型开发测试了预先指定的犯罪史、社会人口统计学和临床风险因素。采用里程碑式方法获取事件时间数据，我们模拟了社区监督期间新医疗保健事件的影响、被监督个体环境的变化以及犯罪抑制的影响。我们在地理上不同的人群中验证了该模型。结果随访期间，有18307人（31%）被重新定罪，4416人（7%）被暴力犯罪，5381人（9%）因精神科诊断住院。该模型具有较强的校正和判别能力（对暴力再犯c-index = 0.74，对一般再犯c-index = 0.69）。它也优于不包含动态数据的比较模型。最后的模型被转换成在线风险计算器（OxMore）。结论动态模型的实施可以为社区监管下的个体提供更准确的风险分层，包括精神疾病和物质使用障碍患者，有可能改善资源分配，并与降低再犯率的干预措施联系起来。（PsycInfo Database Record (c) 2025 APA，版权所有）。

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来源期刊

Law and Human Behavior Multiple-

CiteScore

4.50

自引率

8.00%

发文量

期刊介绍： Law and Human Behavior, the official journal of the American Psychology-Law Society/Division 41 of the American Psychological Association, is a multidisciplinary forum for the publication of articles and discussions of issues arising out of the relationships between human behavior and the law, our legal system, and the legal process. This journal publishes original research, reviews of past research, and theoretical studies from professionals in criminal justice, law, psychology, sociology, psychiatry, political science, education, communication, and other areas germane to the field.