Promoting Rapid Uptake of Multilevel Latent Class Modeling via Best Practices: Investigating Heterogeneity in Daily Substance Use Patterns

通过最佳实践促进多级潜在类建模的快速采用：调查日常物质使用模式的异质性

• 批准号: 10739994
• 负责人: STEPHANIE T LANZA
• 金额: $ 73.37万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10739994
• 关键词: Acceleration; Acute; Adderall; Address; Adoption; Affect; Age; Alcohol consumption; Alcohols; Anxiety; Award; Behavior; Cannabis; Cellular Phone; Characteristics; Chronic; Code; Collection; Complex; Complex Mixtures; Computer software; Data; Data Analyses; Data Set; Demographic Factors; Development; Devices; Disease; Disparity; Drug abuse; Educational workshop; Ensure; Environment; Estimation Techniques; Ethnic Origin; Etiology; Exhibits; Financial Hardship; Guide prevention; Heterogeneity; Individual; Institution; Intervention; Investigation; Knowledge; Life; Manuscripts; Measurement; Methods; Modeling; Outcome; Pattern; Performance; Persons; Policies; Prevention; Psychosocial Factor; Public Health; Race; Research; Research Personnel; Resources; Risk; Risk Factors; Scientist; Sexual and Gender Minorities; Site; Social Characteristics; Specific qualifier value; Statistical Data Interpretation; Stress; Subgroup; Techniques; Time; Translating; United States Substance Abuse and Mental Health Services Administration; Urbanicity; advanced analytics; analytical method; clinically relevant; cost estimate; diaries; high risk; innovation; learning materials; next generation; novel; polysubstance use; psychologic; psychosocial; repository; response; sex; substance misuse; substance use; symposium; therapy design; trait; university student; uptake; vaping; vaping nicotine; webinar; young adult

PROJECT SUMMARY Substance use among young adult college students remains a major public health concern, with recent escalations in nonmedical Adderall use, vaping nicotine, and co-use of alcohol and cannabis. The landscape of substance use continues to evolve, with new modes of use (e.g., vaping) and rapid legislative changes. Daily use behaviors can take many forms, defined by number and type of substances used, mode of use, and quantity used. Further, daily use patterns may be related to both day-level psychosocial factors (e.g., motives, affect, stress) and person-level characteristics (e.g., sex, race/ethnicity, financial strain). Use of multiple substances within a day (“co-use”) is increasingly prevalent and confers high risk for acute and chronic consequences, but is insufficiently studied. The unprecedented amount of high-quality, intensive longitudinal data (ILD) on substance use and associated risk factors holds critical information that can help explain substance use etiology and inform the development of the next generation of interventions that are tailored to those who need it, when they need it. However, advanced methods for analyzing ILD—including multilevel latent class analysis (MLCA) and multilevel latent transition analysis (MLTA)—are needed to characterize the heterogeneity of patterns of use that unfold in daily life and to identify novel day-level intervention targets. These methods enable researchers to discover clinically relevant substance use behavior patterns and day-to- day transitions, dynamic risk factors embedded in daily life that are associated with problematic use patterns, and person-level characteristics that indicate for whom these risk factors are most salient. Yet MLCA and MLTA are underused for ILD analysis, in part due to the dearth of guidance for applied researchers. Guided by a socio-ecological framework, we will evaluate, apply, and disseminate MLCA and MLTA to analyze ILD, considering four ways to specify models: marginal modeling, a fully parametric random effects model, a common factor model, and a non-parametric approach for random effects. We will translate best practices for applying MLCA (Aim 1) and MLTA (Aim 2) to ILD on substance use and co-use. We will conduct coordinated MLCA and MLTA of daily data from two large studies of college students (3CAM: n=343, 15,798 person-days; Project SELF: n=2068, 33,722 person-days). Empirical analyses will disentangle person-level risk factors (e.g., trait anxiety) and dynamic risk factors (e.g., morning affect) to isolate potential novel, dynamic intervention targets. We will examine individual characteristics (e.g., race/ethnicity) as moderators to investigate disparities and identify group-specific mechanisms. To maximize our overall impact (Aim 3) and ensure broad, rigorous adoption of MLCA and MLTA, we propose a broad range of dissemination activities to include online instructional material, a code repository, workshops, and a conference. Findings from studies using these techniques will guide prevention scientists and clinicians in designing interventions targeting the most salient risk factors for specific individuals at the best time for intervention.

项目摘要 年轻的成年大学生中使用药物仍然是一个主要的公共卫生问题，最近 非医疗adderall使用，烟气和酒精和大麻的共同使用中的升级。景观 使用新的使用方式（例如，烟）和快速立法变化，药物使用继续发展。日常的 使用行为可以采用多种形式，由使用的数量和类型定义，使用方式和类型 使用的数量。此外，日常使用模式可能与两个日间的社会心理因素有关（例如，动机， 情感，压力）和人级特征（例如性别，种族/种族，财务压力）。使用倍数 一天之内的物质（“共用”）越来越普遍，并承认急性和慢性的高风险 后果，但不足以研究。前所未有的高质量，密集的纵向 有关物质使用和相关风险因素的数据（ILD）拥有关键信息，可以帮助解释 物质使用病因，并告知下一代干预措施的发展 那些需要它的人，当他们需要时。但是，分析ILD的高级方法 - 包括多级 潜在类别分析（MLCA）和多级潜在过渡分析（MLTA） - 需要表征 在日常生活中展开的使用模式的异质性并确定新颖的日期干预目标。 这些方法使研究人员能够发现临床相关的物质使用行为模式和日常 一日过渡，与问题使用模式相关的日常生活中嵌入的动态风险因素， 以及人级特征，表明这些风险因素最为突出。但是MLCA和 MLTA未用于ILD分析，部分原因是应用研究人员的指导去世。指导 一个社会生态框架，我们将评估，应用和传播MLCA和MLTA来分析ILD， 考虑四种指定模型的方法：边际建模，一个完全参数的随机效应模型，一个 常见因素模型和一种非参数方法的随机效应。我们将翻译最佳实践 应用MLCA（AIM 1）和MLTA（AIM 2），以使用物质使用和共同使用。我们将进行协调 来自两项大型大学生研究的每日数据的MLCA和MLTA（3CAM：n = 343，15,798人日； 项目自我：n = 2068，33,722人日）。经验分析将消除人级风险因素（例如， 特质动画）和动态风险因素（例如，早晨影响），以隔离潜在的新颖，动态干预 目标。我们将研究个人特征（例如种族/种族）作为调查分布的主持人 并确定特定组的机制。为了最大化我们的整体影响（AIM 3）并确保广泛，严格 采用MLCA和MLTA，我们提出广泛的传播活动以包括在线 教学材料，代码存储库，研讨会和会议。研究结果使用这些发现 技术将指导预防科学家和临床医生设计针对最突出的干预措施 在干预的最佳时间中，特定个人的风险因素。