Violence and Victimization in Adults with Mental Illness

描述(由申请人提供)：与普通公众相比，精神病患者实施暴力和成为暴力受害者的风险更大。实施或经历暴力侵害的风险增加，这是一个重大的公共卫生问题。除了由此造成的人身伤害外，暴力事件对肇事者和受害者都是毁灭性的，对公众来说代价高昂。这些事件加速了人身自由的丧失；需要代价高昂的干预；持久的耻辱；并扰乱了护理的连续性。虽然暴力和受害是相互关联的，但这两起事件也与其他因素交织在一起，包括精神病症状、药物使用和服药依从性。然而，很少有研究评论与预防和干预相关的动态风险和保护性因素，或者这些结果的独特和共同的原因和后果。拟议的R01应用程序将采用一种最先进的策略--综合数据分析(IDA)，将使用相同暴力和受害衡量标准的五项研究(包括由NIMH资助的三项研究)的原始数据结合在一起，并允许为所有独立变量制定一个共同的衡量标准。我们的主要分析方法将依赖于潜在增长曲线模型来检验精神疾病、暴力和受害之间的纵向联系。这里提出的项目将扩大与成人精神疾病中的暴力和受害有关的临床、经验和理论文献。结果有可能产生显著的公共卫生效益，并有助于有意义的临床发展。在短期内，在我们的假设得到支持之前，我们将提交一份治疗有效性RCT(R01)，通过收集原始数据来解决这个二级数据分析项目的设计局限性。然而，我们也期待确定一个耐药亚组(假设5)，对其需要额外的治疗开发和验证工作。因此，我们还计划提交R34(随后是更大规模的随机对照试验)，以开发、实施和评估针对这一高风险、高需求群体的病前状况、动态风险和保护因素的治疗(S)。总之，通过利用创新的统计、方法和理论方法，拟议的项目是朝着开启精神卫生保健的新时代迈出的重要一步，以改善患有SMI的成年人及其所在社区的生活质量。 公共卫生相关性：该项目的成果将对改进检测、临床实践和公共政策，以帮助减少或预防这一弱势群体中的暴力行为产生重要影响。暴力和受害是一个重大的公共卫生问题，因为与一般人群相比，精神病患者的这两种情况的发生率要高得多。二次数据分析是扩展现有数据的产量和发展新知识的一种流行且经济高效的方法。这项应用是一项重要的独立贡献，有可能改善公共健康和安全，并促进我们对精神疾病、暴力和受害行为的临床和理论理解。我们将通过不断评估和完善临床相关的理论模型，使用多步骤理论到治疗的研究过程来实现这些目标。

DESCRIPTION (provided by applicant): Persons with mental illness are at increased risk of perpetrating and being the victims of violence compared to members of the general public. This increased risk for perpetrating or experiencing violent victimization is a substantial public health concern. Aside from the resultant physical harm, violent events are ruinous to its perpetrators and victims and costly to the public. These events precipitate the loss of personal liberty; necessitate expensive interventions; perpetuate stigma; and disrupt continuity of care. While violence and victimization are related to each other, both events are also intertwined with other factors, including psychotic symptoms, substance use, and medication adherence. Still, few studies comment on dynamic risk and protective factors relevant to prevention and intervention, or the unique and shared causes and consequences of these outcomes. The proposed R01 application will employ a state-of-the-art strategy, integrated data analysis (IDA), to combine raw data from five studies (including three funded by NIMH) that used the same measures of violence and victimization, and allow for the development a common metric for all independent variables. Our main analytic approach will rely on latent growth curve modeling to examine longitudinal associations between mental illness, violence, and victimization. The project proposed herein will expand the clinical, empirical and theoretical literature relating to violence and victimization among adults with mental illness. Outcomes have the potential to yield significant public health benefits and to contribute to meaningful clinical developments. In the short-term, pending support of our hypotheses, we will submit a treatment effectiveness RCT (R01) addressing the design limitations of this secondary data analysis project through collection of original data. However, we also anticipate identifying a treatment resistant subgroup (Hypothesis 5), for whom additional treatment development and validation work would be needed. As such, we also plan to submit an R34 (followed by a larger scale RCT) to develop, implement and evaluate treatment(s) that target premorbid conditions and dynamic risk and protective factors in this high risk, high need group. In sum, by utilizing innovative statistical, methodological and theoretical approaches, the proposed project is an important step towards ushering in a new era of mental healthcare to improve quality of life for adults with SMI and the communities in which they reside. PUBLIC HEALTH RELEVANCE: Outcomes from this project, in which we propose to combine raw data from five independent studies to assess the prevalence, antecedents, consequences and treatment outcomes for violence and victimization among adults with SMI, will have important implications for improving detection, clinical practice and public policy to help reduce or prevent violence in this vulnerable population. Violence and victimization represent significant public health concerns as the prevalence of each is significantly higher in persons with mental illness compared to rates found in the general population. Secondary data analysis is a popular and cost-efficient way to extend the yield of existing data and develop new knowledge. This application is an important stand-alone contribution with the potential to improve public health and safety, and to advance our clinical and theoretical understanding of mental illness, violence and victimization. We will accomplish these goals through ongoing evaluation and refinement of a clinically-relevant theoretical model, using a multi-step theory-to-treatment research process.