Beaudry Gabrielle, Yu Rongqin, Alaei Arash, Alaei Kamiar, Fazel Seena
Department of Psychiatry, University of Oxford, Oxford, United Kingdom.
Department of Health Care Administration, California State University Long Beach, Long Beach, CA, United States.
Front Psychiatry. 2022 Apr 25;13:805141. doi: 10.3389/fpsyt.2022.805141. eCollection 2022.
Although around 70% of the world's prison population live in low- and middle-income countries (LMICs), risk assessment tools for criminal recidivism have been developed and validated in high-income countries (HICs). Validating such tools in LMIC settings is important for the risk management of people released from prison, development of evidence-based intervention programmes, and effective allocation of limited resources.
We aimed to externally validate a scalable risk assessment tool, the Oxford Risk of Recidivism (OxRec) tool, which was developed in Sweden, using data from a cohort of people released from prisons in Tajikistan. Data were collected from interviews (for predictors) and criminal records (for some predictors and main outcomes). Individuals were first interviewed in prison and then followed up over a 1-year period for post-release violent reoffending outcomes. We assessed the predictive performance of OxRec by testing discrimination (area under the receiver operating characteristic curve; AUC) and calibration (calibration statistics and plots). In addition, we calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for different predetermined risk thresholds.
The cohort included 970 individuals released from prison. During the 12-month follow-up, 144 (15%) were reincarcerated for violent crimes. The original model performed well. The discriminative ability of OxRec Tajikistan was good (AUC = 0.70; 95% CI 0.66-0.75). The calibration plot suggested an underestimation of observed risk probabilities. However, after recalibration, model performance was improved (Brier score = 0.12; calibration in the large was 1.09). At a selected risk threshold of 15%, the tool had a sensitivity of 60%, specificity of 65%, PPV 23% and NPV 90%. In addition, OxRec was feasible to use, despite challenges to risk prediction in LMICs.
In an external validation in a LMIC, the OxRec tool demonstrated good performance in multiple measures. OxRec could be used in Tajikistan to help prioritize interventions for people who are at high-risk of violent reoffending after incarceration and screen out others who are at lower risk of violent reoffending. The use of validated risk assessment tools in LMICs could improve risk stratification and inform the development of future interventions tailored at modifiable risk factors for recidivism, such as substance use and mental health problems.
尽管全球约70%的监狱人口生活在低收入和中等收入国家（LMICs），但针对刑事累犯的风险评估工具却是在高收入国家（HICs）开发和验证的。在LMICs环境中验证此类工具对于出狱人员的风险管理、基于证据的干预计划的制定以及有限资源的有效分配至关重要。
我们旨在使用来自塔吉克斯坦出狱人员队列的数据，对一种可扩展的风险评估工具——牛津累犯风险（OxRec）工具进行外部验证，该工具是在瑞典开发的。数据通过访谈（获取预测因素）和犯罪记录（获取一些预测因素和主要结果）收集。个体首先在监狱中接受访谈，然后在出狱后的1年时间里对其暴力再犯罪结果进行随访。我们通过测试区分度（受试者操作特征曲线下面积；AUC）和校准度（校准统计量和图表）来评估OxRec的预测性能。此外，我们针对不同的预定风险阈值计算了敏感度、特异度、阳性预测值（PPV）和阴性预测值（NPV）。
该队列包括970名出狱人员。在12个月的随访期间，144人（15%）因暴力犯罪再次入狱。原始模型表现良好。OxRec塔吉克斯坦版本的区分能力良好（AUC = 0.70；95%CI 0.66 - 0.75）。校准图显示观察到的风险概率被低估。然而，重新校准后，模型性能得到改善（Brier分数 = 0.12；总体校准度为1.09）。在选定的15%风险阈值下，该工具的敏感度为60%，特异度为65%，PPV为23%，NPV为90%。此外，尽管在LMICs中进行风险预测存在挑战，但OxRec使用起来是可行的。
在LMICs的一项外部验证中，OxRec工具在多项指标上表现良好。OxRec可用于塔吉克斯坦，以帮助对出狱后有暴力再犯罪高风险的人员优先进行干预，并筛选出暴力再犯罪风险较低的其他人员。在LMICs中使用经过验证的风险评估工具可以改善风险分层，并为未来针对累犯的可改变风险因素（如药物使用和心理健康问题）量身定制的干预措施的制定提供依据。
