High-Intensity Drinking over the Life Course: Identifying High-Risk Subgroups through Event-based and Population-Based Approaches

生命历程中的高强度饮酒：通过基于事件和基于人群的方法识别高风险亚组

• 批准号: 10583491
• 负责人: Camillia K. Lui
• 金额: $ 17.72万
• 依托单位: PUBLIC HEALTH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 1981
• 资助国家: 美国
• 起止时间: 1981-07-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10583491
• 关键词: Acute; Adult; Age; Alcohol abuse; Alcohol consumption; Alcoholic beverage heavy drinker; Alcohols; American; Attention; Beverages; Birth; Blood alcohol level measurement; Characteristics; Clinical; Cohort Effect; Consumption; Data; Development; Disparity; Disparity population; Early Intervention; Education; Elderly; Emergency department visit; Ensure; Epidemiology; Ethnic Origin; Event; Family history of; Future; Gender; General Population; Goals; Guidelines; Individual; Intake; Intervention; Knowledge; Life Cycle Stages; Location; Machine Learning; Measurement; Methodology; Mission; Modeling; National Institute on Alcohol Abuse and Alcoholism; Online Systems; Outcome; Pattern; Persons; Population; Prevalence; Prevention strategy; Public Health; Race; Recording of previous events; Recovery; Reporting; Research; Research Project Grants; Risk; Risk Assessment; Risk Estimate; Risk Factors; Sampling; Series; Shapes; Socioeconomic Factors; Statistical Methods; Subgroup; Surveys; Time trend; Woman; Work; adverse outcome; alcohol epidemiology; alcohol measurement; alcohol poisoning; alcohol related problem; alcohol use disorder; binge drinking; cohort; design; diaries; drinking; early screening; ethnic minority; high intensity drinking; high risk; high risk population; improved; machine learning method; machine learning model; men; middle age; population based; preference; protective factors; public health relevance; racial minority; response; social disparities; theories; translational impact; treatment strategy; trend; young adult

ABSTRACT: High-Intensity Drinking over the Life Course: Identifying high-risk subgroups through event-based and population-based approaches Binge or heavy episodic drinking (HED) of ≥4/ ≥5 drinks for women/men in a single occasion or day has been a key marker of harmful drinking in general and clinical populations and has been widely used in alcohol epidemiology to capture the public health burden from alcohol use. Yet critics have raised the issue that the HED threshold fails to capture the impact of high-intensity drinking (HID) and implies the same average risks to all heavy drinkers regardless of whether they consume 5 or 15 drinks. Recent studies have raised attention to HID of consuming double (8+/10+ drinks) or even triple (12+/15+ drinks) the HED amount. As amounts per day reach extreme levels, risks for consequences and alcohol use disorders (AUD) increase greatly, even when average intake remains the same. This proposed Center project will trace trends in harmful drinking patterns with special attention to HID occurrence and rates over a 40-year period, identify a range of alcohol-related precursors and problems through event-based and population-based approaches, and situate harmful patterns within contexts to inform early screening and interventions for high-risk groups. We will employ sophisticated statistical methods to examine associations and consequences involved with HED and HID, using two adult general population and two heavy drinker event-based samples, each type of data designed to generate complementary and new knowledge about HID. We carry forward ARG's rich history in alcohol epidemiology and measurement expertise, and the Center mission to reduce alcohol-related disparities, in all our study aims. In response to NIAAA's priorities, and to ensure direct translational impact of study findings, we incorporate specific theory and practice for each study aim. Our definitions of HED/HID include both threshold (5+, 8+ and 12+) and amounts drunk in any day (from 5-7, 8-11, and 12+ drinks). Aim 1 takes a life course perspective to document harmful drinking trends over time from 1984 to 2024 and by priority groups (e.g., women; middle-aged to older adults; socially disadvantaged groups including racial/ethnic minorities and those with lower education; and people with history of AUD treatment or in recovery). Aim 2 characterizes event-level factors that heighten the risks for HID compared to HED levels. Sensitivity analyses will compare whether adjustments for drink size/strength and drinking rate (estimated BAC) will improve accuracy of how HID predict alcohol problems (e.g., AUD). Aim 3 situates harmful drinking patterns involving HID within the socioecological model to examine risk and protective factors both currently and across the life course that are strongly associated with HID. Also in Aim 3 we assess the extent that HID elevates the public health burden of AUD and other problems above that of HED and non-HED levels for all drinkers and priority groups using risk models and machine learning methods. Study findings will help identify key population subgroups and risk/protective factors that can support future development of targeted interventions at the event, individual, and environmental levels to reduce heavy and high-intensity drinking and their associated consequences.

摘要：生命历程中的高强度饮酒：通过以下方式识别高风险亚组： 基于事件和基于人群的方法 女性/男性在单一场合或一天内狂饮或大量饮酒 (HED) ≥4/≥5 杯已被视为 一般和临床人群中有害饮酒的关键标志，已广泛用于酒精检测 然而，批评者提出了 HED 的问题。 阈值未能捕捉到高强度饮酒 (HID) 的影响，并且意味着所有人面临相同的平均风险 酗酒者无论喝了 5 杯还是 15 杯酒，最近的研究引起了人们对 HID 的关注。 每天饮用 HED 量的两倍（8+/10+ 饮料）甚至三倍（12+/15+ 饮料）。 即使处于平均水平，极端水平、后果和酒精使用障碍 (AUD) 的风险也会大大增加 拟议的中心项目将追踪有害饮酒模式的趋势，并采取特殊措施。 关注 40 年来 HID 的发生率和发生率，确定一系列与酒精相关的前体，并 通过基于事件和基于人口的方法来解决问题，并将有害模式置于背景中 我们将采用复杂的统计数据，为高危人群的早期筛查和干预提供信息。 使用两个成人一般检查与 HED 和 HID 相关的关联和后果的方法 人口和两个基于重度饮酒者事件的样本，每种类型的数据旨在生成 我们继承了 ARG 在酒精流行病学方面的丰富历史。 和测量专业知识，以及中心的使命是减少与酒精相关的差异，这是我们所有研究的目标。 为了响应 NIAAA 的优先事项，并确保研究结果的直接转化影响，我们纳入 每个研究目标的具体理论和实践我们对 HED/HID 的定义包括阈值（5+、8+ 和）。 12+）和每天的饮酒量（5-7杯、8-11杯和12+杯）。目标 1 从生命历程的角度来确定。 记录 1984 年至 2024 年期间按优先群体（例如妇女、中年人）的有害饮酒趋势 老年人；社会弱势群体，包括少数种族/族裔和教育程度较低的群体； 以及有 AUD 治疗史或正在康复的人）。目标 2 描述了加剧的事件级因素。 HID 风险与 HED 水平相比，敏感性分析将比较是否需要针对饮酒进行调整。 体型/强度和饮酒率（估计 BAC）将提高 HID 预测酒精问题的准确性（例如， 目标 3 将有害饮酒模式 HID 置于涉及检查风险的社会生态模型中。 当前和整个生命周期中与 HID 密切相关的保护因素也在 Aim 中。 3 我们评估 HID 在多大程度上使澳元和其他问题的公共健康负担高于 使用风险模型和机器学习方法，确定所有饮酒者和优先群体的 HED 和非 HED 水平。 研究结果将有助于确定关键人群亚组和可以支持未来的风险/保护因素 在事件、个人和环境层面制定有针对性的干预措施，以减少重度和 高强度饮酒及其相关后果。