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.

项目总结/文摘