Machine learning methods for identifying person-level mechanisms of alcohol use among sexual and gender minority intersections

用于识别性少数群体和性别少数人群中个人饮酒机制的机器学习方法

基本信息

批准号：
10706624
负责人：
Connor J McCabe
金额：
$ 17.34万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-19 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10706624
关键词：
Address Adolescence Adult Advocacy Age Alcohol consumption Alcohols All of Us Research Program Automobile Driving Award Behavior Bisexual Black, Indigenous, People of Color COVID-19 Communities Complex Consensus Cross-Sectional Studies Data Data Set Development Discrimination Disease Disparity Employment Equation Ethnic Origin Female Financial Hardship Foundations Future Gender Goals Growth Health Heterogeneity Heterosexuals Housing Individual Individual Differences Inequity Informal Social Control K-Series Research Career Programs Laws Lesbian Gay Bisexual Link Machine Learning Mediating Mediator Mentors Mentorship Methodology Methods Modeling National Institute on Alcohol Abuse and Alcoholism Neighborhoods Participant Pathway interactions Persons Phase Policies Population Positioning Attribute Prevention Process Public Health Race Recovery Reporting Research Resources Risk Risk Factors Role Sampling Scientist Sexism Sexual and Gender Minorities Social status Social support Stigmatization Stress Subgroup Surveys Symptoms Testing Training Underserved Population United States National Institutes of Health Woman Work addiction alcohol misuse alcohol prevention alcohol risk black women career cisgender coping coronavirus pandemic discrete data experience faculty research forest gender minority gender minority community gender minority group gender minority men global health hate crimes high dimensionality high risk intersectionality machine learning method marginalization minority stress minority stressor non-heterosexual nonbinary novel pandemic disease pandemic impact pandemic stress post-pandemic pre-pandemic programs psychologic public health intervention racism random forest sex sexual minority social stressor substance use theories transgender transgender women women of color young adult young woman

项目摘要

The long-term objective of this Pathway to Independence Award is to support candidate Dr. McCabe in building an independent research program and to facilitate his transition into an independent faculty research position. To date, Dr. McCabe’s research has focused on 1.) refining quantitative methods applied in addictions research, and 2.) understanding individual differences in stress, developing self-regulation, and their associations with alcohol use (AU) among sexual minority and non-minority communities. Dr. McCabe seeks to expand his training in AU development, minority stress theory, and applied quantitative methods to a new emphasis on intersectionality and sexual and gender minority (SGM) AU risk, machine learning and multilevel methodologies, and ecological factors influencing AU disparities. This long-term objective will be achieved through a five-year training plan involving a carefully selected mentorship team as well as targeted coursework and hands-on training experiences. The goals of the proposed research are to 1) distinguish SGM subgroups and intersections at heightened risk for AU (e.g., bisexuals and trans persons, SGM young women of color), 2) assess the role of state policies in moderating AU risk, and 3) delineate moderators and mechanisms of heightened AU across SGM populations within and beyond the coronavirus pandemic. The mentored phase (K99) will involve cross-sectional analysis of the All of Us Research Program (AURP), a large (N=331,360) and diverse national dataset. Aim 1 will identify heterogeneity in alcohol and other substance use behaviors among sexual (1a; n=38,820 non-heterosexual) and gender minority (1b; n=2,660 transgender or nonbinary) communities. It will then test race/ethnicity and age as intersectional moderators of SGM inequities (1c) and state-level policies impacting SGM communities (1d; e.g., hate crime laws enumerating SGM identity) that further differentiate AU risk among SGM groups. During the independent phase, findings will be extended to address mediators and moderators of AU in the monthly AURP COVID-19 Participant Experience Survey (Aim 2; n=100,340) as well as the longitudinal, biennial AURP data that extends beyond the pandemic into 2027 (Aim 3). Aim 2 will test pandemic stressors as mediators of between-person AU among SGM intersections (2a) and examine intersectional (2b) and multilevel moderators (2c) of within-person AU. Aim 3 will test differences in post-pandemic recovery in AU among SGM intersections (3a) and determine pandemic mediators (3b) and moderators (3c) of this change. Findings will serve as the foundation for an NIAAA R01 submission during the R00 phase focused on geocoded neighborhood-level factors influencing developing alcohol risk across adolescence and young adulthood across SGM intersections. Mentors (Drs. Rhew, Lee, Helm) and consultants (Drs. Grimm, Bauer, Raifman) are committed to the candidate’s training, each providing unique expertise to the research and training plan. This award will support the candidate’s development as an independent cross-disciplinary prevention scientist in AU disparities and quantitative methods.

独立奖的长期目标是支持候选人麦凯布博士构建独立研究计划并促进他向独立面孔的过渡迄今为止，麦凯布博士的研究重点是1.）加法研究和2。）了解压力中的个体差异，发展自我调节及其麦凯布博士与性少数群体和非少数群体之间的饮酒（AU）相关联。扩大他在AU发展，少数派压力理论和应用定量方法的培训中强调交叉性，性和性别少数群体（SGM）AU风险，机器学习和多层次方法和生态因素影响了AU差异。通过五年培训计划的互动互动，作为精心选择的指导团队以及有针对性的课程和动手培训经验。以及以au风险增加（例如，双性恋和跨性别者，有色人种的年轻女性）的相互作用，2）评估国家政策在调节AU风险中的作用，以及3）描述主持人和机制在冠状病毒大流行期内和超越SGM种群的AU （K99）将涉及我们所有人的研究计划（AURP），大型（n = 331,360）和各种国家数据集。性（1a; n = 38,820非异性恋）和性别少数（1B; n = 2,660跨性别或非二元）社区。影响SGM社区的州级政策（1d;例如，涉嫌SGM身份的仇恨犯罪法）进一步区分SGM组的AU风险。在每月的Aurp Covid-19参与者体验调查中向AU的调解人和主持人致辞（AIM 2; n = 100,340）以及纵向双年展数据，超出了大流行（AIM 3）AIM 2。（2a）并检查au内部AIM 3的相交（2B）和多刀主持人（2C）。 SGM交叉口（3A）中大流行后恢复的差异并确定大流行介体（3b）和主持人（3C）作为ANIAA R01的基础在R00阶段提交的提交，重点是地理编码的邻里级别因素影响发展在SGM交叉口的青春期和成年期的酒精风险。 Helm）和顾问（Grimm，Bauer，Raifman博士）致力于候选人的培训研究和培训计划的独特专业知识将支持候选人的发展独立的跨学科预防科学家在AU差异和定量方法中。