Modeling the Social Environmental Influences and Mechanisms of Suicide

自杀是一个主要的公共卫生问题，具有复杂的病因根源，由于生产力和医疗保健的损失以及对医疗和公共卫生干预措施的抵制而造成的巨大社会成本。以前的研究涉及广泛的风险和保护因素，并记录了对社会环境的强烈影响。然而，研究既没有1）确定社会环境的哪些层面（例如，人际关系、家庭、邻里、宏观环境）在生命过程的不同阶段对女性和男性的自杀结果影响最大，2）社会环境背景如何相互作用和/或与个人行为、认知和生物因素（例如，酒精和药物使用，精神疾病，生活事件）。由于传统上和独特地阻止跨学科视角的自杀研究的科学整合的理论、方法和统计障碍，研究倾向于沿着沿着社会科学/公共卫生和临床/生物学路线分叉。该项目提出了一个创新的解决方案，通过汇集大学和政府中的社会，行为，统计和生物医学科学家的跨学科团队来整合社交网络的理论进步，通过将新的联邦努力（例如，CDC的全国暴力数据报告系统，人口普查局的美国社区研究），并采用考虑到数据复杂性的校正和分析技术（例如，合并数据库、右删失数据、嵌套数据水平）。具体来说，目标我量身定制的网络事件模型III-R发展个人，上下文和多层次的假设，社会环境水平和自杀。Aim II汇编了一套开创性的数据集，将相对较新的联邦数据工作和使用适当的统计校正和新颖的先进技术测试假设联系起来。Aim III评估新方法的优势和局限性，制定计划，通过与联邦机构和州死亡登记系统/官员合作来填补剩余的数据空白，并与生物医学伦理学家合作，确保整个项目的人类受试者保护。与合作已经从疾病预防控制中心和其他关键联邦机构，这项研究计划消除了长期存在的数据和分析障碍，自杀研究，导致在理解的作用，多个社会背景，一个有前途的理论机制（社会网络）连接远端和近端通路，社会和生物/个人因素的相互作用的重大进步。同时致力于这三个目标，可以立即在数据丰富方面取得进展，以检验假设，并同时注意规划未来的联邦数据收集。跨学科理论的完善，数据汇编、校正和概念操作的创新，以及统计方法的严格结合，将为针对自杀和其他健康结果的临床和社区干预措施提供更好的知识和科学基础。

DESCRIPTION (provided by applicant): Suicide stands as a major public health problem with complex etiological roots, tremendous societal cost through lost productivity and medical care, and resistance to medical and public health interventions. Previous research has implicated a wide range of risk and protective factors, with strong effects documented for the social environment. However, research has neither 1) identified which levels of the social environment (e.g., interpersonal relationships, families, neighborhoods, macro-environments) are most influential in suicide outcomes for women and men at different stages of the life course nor 2) how social environmental contexts interact with each other and/or with individual behavioral, cognitive, and biological factors (e.g., alcohol and drug use, mental illness, life events). Research tends to be bifurcated along social science/public health and clinical/biological lines due to theoretical, methodological and statistical barriers that have traditionally and uniquely prevented scientific integration of suicide research across disciplinary perspectives. This project proposes an innovative solution to this stalemate by bringing together a trans-disciplinary team of social, behavioral, statistical and biomedical scientists in universities and in government to integrate theoretical advances in social networks, overcome data barriers by linking data from new federal efforts (e.g., CDC's National Violence Data Reporting System, Census Bureau's American Community Study), and employ corrections and analytic techniques that take into account data complexity (e.g., merged data bases, right censored data, nested data levels). Specifically, Aim I tailors the Network Episode Model III-R to develop individual, contextual and multi-level hypotheses linking social environmental levels and suicide. Aim II compiles a pioneering data set linking relatively new federal data efforts and testing hypotheses using appropriate statistical corrections and novel advanced techniques. Aim III assesses strengths and limits of the new approach, develops plans to fill remaining data gaps by working with federal agencies and state death registration systems/officials, and works with biomedical ethicists to ensure human subject protections across the entire project. With cooperation already secured from the CDC and other key federal agencies, this research plan eliminates longstanding data and analytic barriers in suicide research, leading to significant advancements in understanding the role of multiple social contexts, a promising theoretical mechanism (social network) linking distal and proximal pathways, and interaction of social and biological/individual factors. Working simultaneously on the three aims allows immediate advancement in data enrichment for testing hypotheses and concurrent attention to planning future federal data collections. The refinement of trans- disciplinary theory, innovation in data compilation, correction and concept operationalization, and the rigorous combination of statistical methods will improve knowledge and scientific foundations for clinical and community-based interventions targeted to suicide and other health outcomes.