Environmental factors and social adjustment as predictors of a first psychosis in subjects at ultra high risk
Environmental factors and social adjustment as predictors of a first psychosis in subjects at ultra high risk
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DOI：
10.1016/j.schres.2010.09.007
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发表时间：
2011-01-01
影响因子：
4.5
通讯作者：
Linszen, Don H.
中科院分区：
文献类型：
--
作者：
Dragt, Sara;Nieman, Dorien H.;Linszen, Don H.
背景：精神分裂症的发病与遗传、症状、社会和环境等危险因素有关。本研究的目的是确定哪些环境因素可能有助于预测超高风险(UHR)受试者发展为精神病的第一次精神病发作。方法：我们包括72名UHR受试者，并对他们进行了长达36个月的跟踪调查，其中19人(26.4%)转变为精神病。我们应用生存分析来确定向精神病的转变与环境因素和社会适应之间的联系。为了确定哪些项目是向第一次精神病发作转变的最好预测因素，应用了COX回归分析。结果：都市性、领取国家福利和病前适应不良(PMA)显著影响向精神病转变。都市性(WALD=10.096，p=.001，HR=30.97)、社会性(WALD=8.795，P=.003，HR=1.91)和社会-个人适应(WALD=10.794，P=.001，HR=4.26)似乎是UHR人群中发展成精神病的预测因素。这些特征应该在预测精神病的模型中实现。这样的模式将比目前的模式更具体，并可能导致针对患者的预防干预。(C)2010爱思唯尔B.V.保留所有权利。
BACKGROUND: The onset of schizophrenia is associated with genetic, symptomatic, social and environmental risk factors. The aim of the present study was to determine which environmental factors may contribute to a prediction of a first psychotic episode in subjects at Ultra High Risk (UHR) for developing psychosis.METHOD: We included 72 UHR subjects and followed them over a period of 36 months, of whom nineteen (26.4%) made a transition to psychosis. We applied survival analyses to determine associations between a transition to psychosis and environmental factors and social adjustment. To determine which items are the best predictors of transition to a first psychotic episode, Cox Regression analyses were appliedRESULTS: Urbanicity, receiving state benefits and poor premorbid adjustment (PMA) significantly influenced the transition to psychosis. Urbanicity (Wald = 10.096, p = .001, HR = 30.97), social-sexual aspects (Wald = 8.795, p = .003, HR = 1.91) and social-personal adjustment (Wald = 10.794, p = .001, HR = 4.26) appeared to be predictors for developing psychosis in our UHR group.CONCLUSIONS: Environmental characteristics and social adjustment are predictive of transition to a psychosis in subjects at UHR. These characteristics should be implemented in a model for prediction of psychosis. Such a model would be more specific than current models and may lead to patient-specific preventive interventions.(C) 2010 Elsevier B.V. All rights reserved.