Identification of Prospective Predictors of Alcohol Initiation During Early Adolescence

青春期早期饮酒的前瞻性预测因素的鉴定

• 批准号: 10823917
• 负责人: Andrew Moore
• 金额: $ 4.21万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10823917
• 关键词: 10 year old; Address; Adolescent; Age; Alcohol consumption; Alcohols; American; Attitude; Brain; Child; Classification; Consumption; Data; Data Analyses; Data Set; Development; Diagnosis; Early Intervention; Environmental Impact; Environmental Risk Factor; Equation; Factor Analysis; Future; Goals; Individual; Intervention; Investigation; Life; Literature; Machine Learning; Measures; Mediating; Methods; Modeling; National Institute on Alcohol Abuse and Alcoholism; Neurocognitive; Outcome; Participant; Predictive Factor; Prevention; Research; Research Personnel; Rewards; Risk; Risk Factors; Sampling; Techniques; Testing; Time; Weight; Work; Youth; alcohol availability; alcohol risk; alcohol use disorder; alcohol use initiation; cognitive development; data-driven model; early adolescence; machine learning model; multidimensional data; neuroimaging; neurophysiology; novel; peer; preadolescence; prevent; prospective; random forest; underage drinking

Project Summary/Abstract Recent initiatives from the National Institute on Alcohol Abuse and Alcoholism have focused on the prevention of child and adolescent alcohol use. To address this, the proposed project focuses on identifying salient, prospective predictors and inferred causes of early alcohol initiation (EAI) or the consumption of a full drink containing alcohol before the age of 16. The aims of the proposed project will test current assumptions of predictors of alcohol initiation (Aim 1a), compare a priori risk factors with novel, data-driven risk factors (Aim 1b), infer causal relationships between factors and EAI (Aim 2), and, as an exploratory aim, isolate individual measures as predictors using machine learning techniques (Aim 3). To accomplish these aims, we will be leveraging the Adolescent Brain Cognitive Development (ABCD) study consisting of over 11,000 youth participants. ABCD provides multiple time points of neuroimaging, neurocognitive, and environmental measures starting when youth are 9-to-10-years-old allowing for the incorporation of high-dimensional data into a single framework. Thus, the resulting sample provides a unique opportunity to use advanced quantitative methods including structural equation modeling, exploratory factor analysis, and random forest machine learning to highlight what drives youth to initiate alcohol use during this risky developmental period. The outcomes of this project aim to inform research on prevention and intervention of EAI to ultimately delay alcohol initiation to more developmentally appropriate ages.

项目概要/摘要 国家酒精滥用和酒精中毒研究所最近的举措侧重于预防 儿童和青少年饮酒的情况。为了解决这个问题，拟议的项目重点是确定突出的、 早期饮酒 (EAI) 或喝满酒的前瞻性预测因素和推断原因 16 岁之前饮酒。拟议项目的目标将测试当前的假设 饮酒的预测因素（目标 1a），将先验风险因素与新的、数据驱动的风险因素进行比较（目标 1b）， 推断因素与 EAI 之间的因果关系（目标 2），并且作为探索性目标，隔离个体 使用机器学习技术作为预测指标（目标 3）。为了实现这些目标，我们将 利用由超过 11,000 名青少年组成的青少年大脑认知发展 (ABCD) 研究 参与者。 ABCD 提供神经影像、神经认知和环境测量的多个时间点 从 9 到 10 岁的青少年开始，允许将高维数据合并到一个单一的数据中 框架。因此，所得样品提供了使用先进定量方法的独特机会 包括结构方程建模、探索性因素分析和随机森林机器学习 强调是什么促使青少年在这个危险的发展时期开始饮酒。这样做的结果 该项目旨在为 EAI 的预防和干预研究提供信息，最终延迟更多人开始饮酒 发育适宜的年龄。