Huber David A, Lau Steffen, Sonnweber Martina, Günther Moritz P, Kirchebner Johannes
Department of Forensic Psychiatry, Psychiatric Hospital, University of Zürich, 8006 Zurich, Switzerland.
Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, 8091 Zurich, Switzerland.
Int J Environ Res Public Health. 2020 Oct 28;17(21):7922. doi: 10.3390/ijerph17217922.
Migrants diagnosed with schizophrenia are overrepresented in forensic-psychiatric clinics. A comprehensive characterization of this offender subgroup remains to be conducted. The present exploratory study aims at closing this research gap. In a sample of 370 inpatients with schizophrenia spectrum disorders who were detained in a Swiss forensic-psychiatric clinic, 653 different variables were analyzed to identify possible differences between native Europeans and non-European migrants. The exploratory data analysis was conducted by means of supervised machine learning. In order to minimize the multiple testing problem, the detected group differences were cross-validated by applying six different machine learning algorithms on the data set. Subsequently, the variables identified as most influential were used for machine learning algorithm building and evaluation. The combination of two childhood-related factors and three therapy-related factors allowed to differentiate native Europeans and non-European migrants with an accuracy of 74.5% and a predictive power of AUC = 0.75 (area under the curve). The AUC could not be enhanced by any of the investigated criminal history factors or psychiatric history factors. Overall, it was found that the migrant subgroup was quite similar to the rest of offender patients with schizophrenia, which may help to reduce the stigmatization of migrants in forensic-psychiatric clinics. Some of the predictor variables identified may serve as starting points for studies aimed at developing crime prevention approaches in the community setting and risk management strategies tailored to subgroups of offenders with schizophrenia.
被诊断患有精神分裂症的移民在法医精神病诊所中的占比过高。对这一犯罪亚群体进行全面描述的工作仍有待开展。本探索性研究旨在填补这一研究空白。在瑞士一家法医精神病诊所收治的370例精神分裂症谱系障碍住院患者样本中，分析了653个不同变量，以确定欧洲本土人与非欧洲移民之间可能存在的差异。探索性数据分析通过监督式机器学习进行。为尽量减少多重检验问题，对检测到的组间差异在数据集上应用六种不同的机器学习算法进行交叉验证。随后，将被确定为最具影响力的变量用于机器学习算法的构建和评估。两个与童年相关的因素和三个与治疗相关的因素相结合，能够以74.5%的准确率和AUC = 0.75（曲线下面积）的预测能力区分欧洲本土人和非欧洲移民。任何所研究的犯罪史因素或精神病史因素都无法提高AUC。总体而言，研究发现移民亚群体与其他患有精神分裂症的犯罪患者相当相似，这可能有助于减少法医精神病诊所中对移民的污名化。所确定的一些预测变量可作为旨在制定社区环境中犯罪预防方法以及针对精神分裂症犯罪亚群体的风险管理策略的研究起点。
