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识别出院患者暴力行为的因果风险因素。

Identifying Causal Risk Factors for Violence among Discharged Patients.

作者信息

Coid Jeremy W, Kallis Constantinos, Doyle Mike, Shaw Jenny, Ullrich Simone

机构信息

Violence Prevention Research Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London. Garrod Building, Turner Street, London E1 2AD, United Kingdom.

Institute of Brain, Behaviour and Mental Health, University of Manchester. Oxford Road, Manchester M13 9PL, United Kingdom.

出版信息

PLoS One. 2015 Nov 10;10(11):e0142493. doi: 10.1371/journal.pone.0142493. eCollection 2015.

Abstract

BACKGROUND

Structured Professional Judgement (SPJ) is routinely administered in mental health and criminal justice settings but cannot identify violence risk above moderate accuracy. There is no current evidence that violence can be prevented using SPJ. This may be explained by routine application of predictive instead of causal statistical models when standardising SPJ instruments.

METHODS

We carried out a prospective cohort study of 409 male and female patients discharged from medium secure services in England and Wales to the community. Measures were taken at baseline (pre-discharge), 6 and 12 months post-discharge using the Historical, Clinical and Risk-20 items version 3 (HCR-20v3) and Structural Assessment of Protective Factors (SAPROF). Information on violence was obtained via the McArthur community violence instrument and the Police National Computer.

RESULTS

In a lagged model, HCR-20v3 and SAPROF items were poor predictors of violence. Eight items of the HCR-20v3 and 4 SAPROF items did not predict violent behaviour better than chance. In re-analyses considering temporal proximity of risk/ protective factors (exposure) on violence (outcome), risk was elevated due to violent ideation (OR 6.98, 95% CI 13.85-12.65, P<0.001), instability (OR 5.41, 95% CI 3.44-8.50, P<0.001), and poor coping/ stress (OR 8.35, 95% CI 4.21-16.57, P<0.001). All 3 risk factors were explanatory variables which drove the association with violent outcome. Self-control (OR 0.13, 95% CI 0.08-0.24, P<0.001) conveyed protective effects and explained the association of other protective factors with violence.

CONCLUSIONS

Using two standardised SPJ instruments, predictive (lagged) methods could not identify risk and protective factors which must be targeted in interventions for discharged patients with severe mental illness. Predictive methods should be abandoned if the aim is to progress from risk assessment to effective risk management and replaced by methods which identify factors causally associated with violence.

摘要

背景

结构化专业判断(SPJ)在心理健康和刑事司法环境中经常使用,但识别暴力风险的准确性仅为中等以上。目前没有证据表明使用SPJ可以预防暴力。这可能是由于在标准化SPJ工具时常规应用预测性而非因果性统计模型所致。

方法

我们对409名从英格兰和威尔士中等安全级别的服务机构出院并回到社区的男性和女性患者进行了一项前瞻性队列研究。在基线(出院前)、出院后6个月和12个月时,使用历史、临床和风险-20项第3版(HCR-20v3)和保护因素结构评估(SAPROF)进行测量。通过麦克阿瑟社区暴力工具和警方国家计算机获取暴力信息。

结果

在一个滞后模型中,HCR-20v3和SAPROF项目对暴力的预测能力较差。HCR-20v3的8个项目和SAPROF的4个项目对暴力行为的预测并不比随机猜测更好。在重新分析中,考虑到风险/保护因素(暴露)与暴力(结果)的时间接近性,由于暴力观念(比值比6.98,95%置信区间13.85-12.65,P<0.001)、不稳定(比值比5.41,95%置信区间3.44-8.50,P<0.001)和应对/压力差(比值比8.35,95%置信区间4.21-16.57,P<0.001),风险升高。所有这3个风险因素都是驱动与暴力结果关联的解释变量。自我控制(比值比0.13,95%置信区间0.08-0.24,P<0.001)具有保护作用,并解释了其他保护因素与暴力的关联。

结论

使用两种标准化的SPJ工具,预测性(滞后)方法无法识别出严重精神疾病出院患者干预中必须针对的风险和保护因素。如果目标是从风险评估推进到有效的风险管理,预测性方法应被摒弃,取而代之的是识别与暴力有因果关联因素的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb36/4640710/8431e226c3bc/pone.0142493.g001.jpg

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