Predicting Substance Use among Military Veterans with a Positive MST Screen: A Machine Learning Approach

通过积极的 MST 筛选来预测退伍军人的药物使用情况：一种机器学习方法

• 批准号: 10458304
• 负责人: SHANNON FORKUS
• 金额: $ 0.25万
• 依托单位: UNIVERSITY OF RHODE ISLAND
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-24 至 2023-05-23
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10458304
• 关键词: Address; Affect; Algorithms; Area; Big Data; Cessation of life; Clinical; Code; Complex; Data; Data Set; Detection; Development; Distress; Etiology; Exposure to; Goals; Grant; Health; Health behavior; Impairment; Individual; Institution; Intervention; Investigation; Knowledge; Legal; Link; Machine Learning; Maintenance; Manuscripts; Maps; Methodology; Military Personnel; Mission; National Institute of Drug Abuse; Nature; Occupational; Outcome; Output; Overdose; Policies; Population; Population Research; Prevention; Prognosis; Psychology; Public Health; Recording of previous events; Research; Research Methodology; Research Personnel; Research Project Grants; Rhode Island; Risk; Risk Factors; Sampling; Science; Self Medication; Service provision; Sexual Harassment; Statistical Data Interpretation; Statistical Methods; Suicide; Symptoms; Time; To specify; Traffic accidents; Translating; United States Department of Veterans Affairs; Universities; Veterans; career; clinically relevant; cohort; design; experience; high risk; high risk behavior; high risk population; improved; innovation; longitudinal analysis; longitudinal dataset; machine learning method; military service; military veteran; prediction algorithm; predictive modeling; preventive intervention; programs; prospective; sexual assault; sexual trauma; skills; social; stem; substance misuse; substance use; supervised learning; theories; trauma exposure

Project Summary/Abstract Military sexual trauma (MST) is a serious and pervasive problem among military populations, affecting approximately 16% of military personnel and veterans [1]. Substance use disproportionately affects individuals with a history of MST. Individuals with (vs. without) a history of MST are twice as likely to misuse substances [2-4]. Substance use among military samples has been linked to higher rates of negative consequences across several domains (e.g., health, occupational, legal [5, 6]), including death (e.g., overdose [7], traffic accidents [8], suicide [7, 8]). Further, while understudied among individuals with a history of MST in particular, negative substance use outcomes have been shown to be more severe among trauma-exposed populations, including more severe clinical presentations and poorer treatment prognosis [9, 10]. These findings emphasize the importance of clarifying the association between MST and substance use among military populations. Despite the clinical relevance and public health significance of substance use among military populations, research in this area has relied almost exclusively on cross-sectional designs. Moreover, the vast majority of studies in this area have utilized traditional statistical methods, which are limited in scope and capabilities. These limitations have important clinical implications, as they restrict our ability to specify the exact nature and directionality of the relationship between MST and substance use, thereby affecting how findings are translated into prevention and intervention efforts. The proposed research aims to fill these critical gaps by utilizing the Army STARRS pre/post-deployment study, a large, prospective military dataset to: (1) explicate the directional relation between MST and substance use using a longitudinal dataset, and (2) employ machine learning methods to develop an algorithm to optimize detection of substance use in military personnel with a history of MST. These findings will assist in elucidating the etiology of substance use among this high-risk group, as well as provide a prediction model for clinical use to better target at-risk individuals in this population. This research project will take place within the Department of Psychology at the University of Rhode Island; an institution with a strong history and commitment to health behavior research and methodology. The applicant will have access to sponsors and consultants with expertise in MST, substance use, advanced methodology, and statistical analysis that will facilitate her career objectives to develop increased knowledge and proficiency in (a) sexual trauma (e.g., MST) and substance use in military veterans; (b) grant/manuscript development; (c) statistical and methodological capabilities (i.e., machine learning); and (d) big data. The proposed project uses a timely and innovative approach to advance science on the relation between MST and substance use in military personnel. Addressing substance use in this population is necessary to improve the health of our nation's veterans, and aligns with the mission of the National Institute on Drug Abuse.

项目总结/文摘