Expanding a Multi-Level, Longitudinal Database to Assess Ecological Risk Factors in Community Violence

扩展多层次、纵向数据库以评估社区暴力中的生态风险因素

• 批准号: 2314494
• 负责人: Daniel Semenza
• 金额: $ 27.72万
• 依托单位: Rutgers University Camden
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2314494&HistoricalAwards=false
• 关键词: Expanding; Multi; Level; Longitudinal; Database

Each year more than 85,000 people suffer from firearm injuries in the United States. Intentional assaults account for about 40% of all firearm injuries with approximately 12,000 killed each year as a result. Firearm violence is not evenly distributed across the population and the majority of homicide and shooting victims are young men of particular demographic groups living in structurally disadvantaged urban communities. A robust understanding of the macrostructural causes of this violence is necessary to inform violence reduction policies in the nation’s most affected communities. This study will extend prior research by considering how ecological risk factors operate in concert with one another and correspond reciprocally to rates of firearm violence to generate persistent cycles that harm communities over time. Findings will significantly contribute to the advancement of knowledge to inform the development of effective policies and programs to prevent firearm violence in communities. Moreover, it will generate a comprehensive neighborhood-level dataset that can be made publicly available to researchers for continued study on the ecological correlates of firearm violence in local communities. Investigators will construct a comprehensive database and examine the multilevel and reciprocal ecological causes of community firearm violence in almost 16,000 neighborhoods in 100 U.S. cities over ten years. The research will include an integrated series of longitudinal, multivariate analyses alongside coincidence analysis (CNA), a novel cross-case method for causal inference, to generate greater knowledge of the ecological factors that drive cycles of firearm violence in local spaces and related community disparities. The investigators will first analyze a series of multilevel models of neighborhood-, city-, and state-level risk factors to identify key correlates and multifactorial pathways that predict rates of local firearm homicides and non-fatal shootings over time. Following this, the researchers will assess the reciprocal relationship between community conditions and rates of firearm violence with particular attention to implications for community disparities. Findings can help prioritize funding and resource provision for federal, state, and local agencies tasked with preventing and addressing community violence as well as federal programs that support group-based violence interventions (GVI’s) documented to successfully help reduce violent crime in high-risk neighborhoods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在美国，每年有超过85，000人遭受枪支伤害。故意攻击占所有火器伤害的40%左右，每年约有12，000人因此丧生。枪支暴力在人口中的分布并不均匀，大多数杀人和枪击受害者是生活在结构上处于不利地位的城市社区的特定人口群体的年轻男子。对这种暴力的宏观结构原因的深刻理解是必要的，以告知该国受影响最严重的社区的减少暴力政策。这项研究将通过考虑生态风险因素如何相互协调运作来扩展先前的研究，并与枪支暴力的发生率相对应，以产生长期危害社区的持续循环。调查结果将大大有助于知识的进步，为制定有效的政策和计划提供信息，以防止社区枪支暴力。此外，它还将生成一个全面的社区级数据集，可供研究人员公开使用，以继续研究当地社区枪支暴力的生态相关性。调查人员将建立一个全面的数据库，并研究十年来美国100个城市近16，000个社区枪支暴力的多层次和相互生态原因。该研究将包括一系列综合的纵向多变量分析以及巧合分析（CNA），这是一种用于因果推理的新型跨案例方法，以更好地了解推动当地空间枪支暴力循环的生态因素以及相关的社区差异。研究人员将首先分析一系列社区、城市和州一级风险因素的多层次模型，以确定关键的相关因素和多因素途径，这些因素和途径可以预测当地枪支凶杀案和非致命枪击案的发生率。在此之后，研究人员将评估社区条件和枪支暴力率之间的相互关系，特别注意对社区差异的影响。调查结果可以帮助优先考虑联邦，州，负责预防和解决社区暴力的地方机构，以及支持基于群体的暴力干预（GVI）的联邦计划，这些计划被记录下来，成功地帮助减少了高暴力犯罪率地区的暴力犯罪，该奖项反映了NSF的法定使命，并被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准。