Multi-site External Validation and Improvement of a Clinical Screening Tool for Future Firearm Violence

未来枪支暴力临床筛查工具的多站点外部验证和改进

• 批准号: 10683780
• 负责人: Jason Elliott Goldstick
• 金额: $ 0.6万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2023-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10683780
• 关键词: Multi; site; External; Validation; Improvement

ABSTRACT Interventions in clinical settings, such as the emergency department (ED), are an opportunity for interpersonal firearm violence prevention, particularly among youth, whom interpersonal firearm violence disproportionately affects. A crucial prerequisite to successful clinical interventions is an accurate gauge of risk, to ensure the judicious allocation of scarce resources; providing that missing prerequisite is the primary goal of the proposed work. Machine learning methods, in contrast to traditional inferential statistical models, are distinguished by their emphasis on prospective prediction, and have enhanced clinical prediction in several fields, including heart disease, cancer diagnosis and outcomes, PTSD, suicide risk, and substance use, among others. Yet, with the exception of the SAFETY score—developed by the current investigative team—machine learning methods have not been leveraged to prospectively predict firearm violence. In this proposed work our research objectives are two-fold: 1) Externally validate the SAFETY score by determining its ability to predict firearm violence involvement within the next year on a new data set; and 2) Improve the SAFETY score by conducting a comparative analysis of four powerful machine learning methods: elastic net penalized logistic regression, random forests, support vector machines, and boosting (ensemble) methods. In this way, we are responding to Objective One: Research to help inform the development of innovative and promising opportunities to enhance safety and prevent firearm-related injuries, deaths, and crime. This approach is innovative because it builds upon the only work to apply machine learning methods to firearm violence prediction, and it is a promising opportunity to prevent firearm injuries because it will a) provide an explicit gauge of future firearm violence risk; and b) characterize risk factor effects in terms of their prospective prediction ability, unlike any prior research. Thus this research will both identify individuals in most need of intervention, and also point to potentially modifiable predictive factors. Properly addressing this research question in a generalizable way requires a contemporary data set with 1) a focus on a high-need, yet broad, study population; 2) comprehensive baseline measures that provide a broad basis for prediction; and 3) geographic variability (Midwest, West Coast, and East Coast) that enhances generalizability. Thus, we will recruit 1,500 youth age 18-24 from urban EDs in three broadly different locales—Flint, Philadelphia, and Seattle—and administer a baseline survey covering several domains of potential risk factors for future violence, and follow up with those youth at 6- and 12-months to ascertain the primary outcome—firearm violence involvement (as victim or perpetrator)—as well as the secondary outcomes: high-risk firearm behaviors, non-firearm violence, and violent injury. Because this work requires a prospective longitudinal study, we are applying for Option B. This work will lay the ground for future research involving the development and testing of interventions for interpersonal firearm violence both by identifying potential high- leverage modifiable predictive factors, and by identifying youth most in need of intervention.

摘要 在临床环境中的干预，如急诊科（艾德），是一个机会， 预防人与人之间的火器暴力，特别是在青少年中， 不成比例的影响。成功的临床干预的一个关键先决条件是准确衡量风险， 确保稀缺资源的明智分配;提供缺失的先决条件是首要目标， 建议的工作。与传统的推理统计模型相比，机器学习方法 其特点是强调前瞻性预测，并在几个领域增强了临床预测， 包括心脏病、癌症诊断和结果、创伤后应激障碍、自杀风险和物质使用等。 然而，除了目前的调查团队开发的安全评分之外，机器学习 尚未利用方法来前瞻性地预测枪支暴力。在这项工作中，我们的研究 目标有两个方面：1）通过确定其预测火器的能力，从外部验证安全评分 在下一年的暴力参与一个新的数据集;和2）提高安全分数，通过进行 四种强大的机器学习方法的比较分析：弹性网惩罚逻辑回归， 随机森林、支持向量机和提升（集成）方法。这样，我们应对 目标一：开展研究，帮助开发创新和有希望的机会， 安全和防止与枪支有关的伤害，死亡和犯罪。这种方法是创新的，因为它建立在 这是唯一一项将机器学习方法应用于枪支暴力预测的工作，这是一个很有前途的机会。 防止火器伤害，因为它将a）提供未来火器暴力风险的明确衡量标准;以及B） 根据其前瞻性预测能力来描述风险因素的影响，这与任何先前的研究不同。因此该 研究将确定最需要干预的个人，并指出潜在的可改变的 预测因素。以一种可推广的方式适当地解决这个研究问题需要一个当代的 数据集：1）重点关注高需求但广泛的研究人群; 2）全面的基线测量， 为预测提供了广泛的基础; 3）地理变异性（中西部、西海岸和东海岸）， 增强了普遍性。因此，我们将从三个不同的城市ED招募1，500名18-24岁的年轻人， 弗林特，费城和西雅图，并管理一项涵盖以下几个领域的基线调查： 未来暴力的潜在风险因素，并在6个月和12个月时对这些青年进行跟踪，以确定 主要结果-枪支暴力参与（作为受害者或肇事者）-以及次要结果： 高风险枪支行为，非枪支暴力和暴力伤害。因为这项工作需要一个前瞻性的 纵向研究，我们申请方案B。这项工作将为今后的研究奠定基础， 制定和测试针对人际枪支暴力的干预措施， 利用可改变的预测因素，并确定最需要干预的青年。