Wu Dongmei, Liu Tingting, Song Quan, Li Changwei, Yue Yuchuan, Yang Junlan, Li Tao, Ye Zixiang
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, 610036 Chengdu, Sichuan, China.
Florida State University College of Nursing, Vivian M. Duxbury Hall, Tallahassee, FL 32306-4310, USA.
Alpha Psychiatry. 2025 Aug 26;26(4):46062. doi: 10.31083/AP46062. eCollection 2025 Aug.
Physical aggression in schizophrenia patients carries significant societal implications. Previous studies on aggression prediction have primarily focused on hospitalized patients, overlooking specific rural community contexts in China. This study investigated multidimensional predictive factors to develop and validate a predictive model for predicting physical aggression in schizophrenia patients in rural communities in southwestern China.
We used convenience sampling to select 889 confirmed patients with schizophrenia from 22 rural townships recorded by the Pengzhou Mental Health Center from September to November, 2019 for baseline survey. Sixty-two candidate factors were investigated using the Morningness-Eveningness Questionnaire, Multidimensional Fatigue Inventory, and Medication Coherence Rating Scale, and aggression was evaluated using the Modified Overt Aggression Scale during a 3-month follow-up. Logistic regression was used to construct a risk prediction model and the model was validated using the receiver operating characteristic (ROC) curve.
Two variable selection methods, least absolute shrinkage and selection operator and multivariate logistic regression, yielded two models: Model 1 with 27 variables and Model 2 with six variables. Both models demonstrated perfect discrimination, good calibration, and clinical utility. Model 2, with three historical and three modifiable factors, demonstrated greater utility for communities, which included physical aggression against others prior to the first episode of schizophrenia, the Modified Overt Aggression Scale score at first episode onset, mental fatigue, body mass index, experiences of discrimination, and aggression against objects before the first episode. Most predictors were non-specific to schizophrenia.
These findings may help to alleviate the social discrimination and constraints faced by individuals with schizophrenia in rural communities, facilitating the provision of proactive mental health services. Furthermore, our results highlight body mass index, discrimination experiences, and mental fatigue as critical areas for rural community mental health nursing professionals.
No: ChiCTR1800015219. https://www.chictr.org.cn/showproj.html?proj=25941.
精神分裂症患者的身体攻击行为具有重大的社会影响。以往关于攻击行为预测的研究主要集中在住院患者身上，忽视了中国农村社区的具体情况。本研究调查了多维度预测因素，以建立并验证一个预测模型，用于预测中国西南部农村社区精神分裂症患者的身体攻击行为。
我们采用便利抽样法，从彭州市精神卫生中心2019年9月至11月记录的22个农村乡镇中选取889例确诊的精神分裂症患者进行基线调查。使用晨型-夜型问卷、多维疲劳量表和药物依从性评定量表对62个候选因素进行调查，并在3个月的随访期间使用改良的公开攻击量表评估攻击行为。采用逻辑回归构建风险预测模型，并使用受试者工作特征（ROC）曲线对模型进行验证。
两种变量选择方法，即最小绝对收缩和选择算子法以及多变量逻辑回归，产生了两个模型：模型1包含27个变量，模型2包含6个变量。两个模型均显示出良好的区分度、校准度和临床实用性。模型2包含三个历史因素和三个可改变因素，对社区具有更大的实用性，其中包括精神分裂症首次发作前对他人的身体攻击、首次发作时的改良公开攻击量表得分、精神疲劳、体重指数、歧视经历以及首次发作前对物体的攻击。大多数预测因素并非精神分裂症所特有。
这些发现可能有助于减轻农村社区精神分裂症患者所面临的社会歧视和限制，促进积极的心理健康服务的提供。此外，我们的结果强调体重指数、歧视经历和精神疲劳是农村社区心理健康护理专业人员的关键关注领域。
编号：ChiCTR1800015219。https://www.chictr.org.cn/showproj.html?proj=2
