Multimethod Examination of Individual and Environmental Factors Associated with Alcohol Use and Behavioral Health Care Disparities Among Racial/Ethnic Minority and Women Veterans

对种族/族裔少数群体和女性退伍军人中与饮酒和行为保健差异相关的个人和环境因素进行多方法检查

• 批准号: 10721113
• 负责人: Jordan P Davis
• 金额: $ 66.05万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721113
• 关键词: Address; African American; Alcohol consumption; Alcohols; Attitude; Behavior; Behavioral; Black race; Caring; Censuses; Chronic; Data; Data Set; Disadvantaged; Discrimination; Disease; Disparity; Economics; Enrollment; Ensure; Environmental Risk Factor; Future; Health care facility; Healthcare; Healthcare Systems; Hispanic; Income; Individual; Inequality; Intervention; Interview; Life; Link; Longitudinal Studies; Longitudinal cohort; Measurement; Mental Depression; Methods; Military Personnel; Minority; Minority Women; Modeling; Neighborhoods; Not Hispanic or Latino; Outcome; Policies; Post-Traumatic Stress Disorders; Poverty; Prevention; Race; Recommendation; Recording of previous events; Reporting; Research; Role; Sampling; Series; Sex Discrimination; Sexism; Sexual Harassment; Surveys; Symptoms; System; Testing; Time; Veterans; Veterans Health Administration; Violence; Woman; Work; alcohol research; behavior influence; behavior prediction; behavioral health; care systems; catalyst; demographics; deprivation; distilled alcoholic beverage; ethnic diversity; ethnic minority; ethnic minority population; experience; follow-up; health care availability; health care disparity; health care service; health care service utilization; health difference; health disparity; innovation; machine learning method; machine learning model; male; men; microaggression; military trauma; military veteran; outreach; perceived discrimination; post 9/11; racial discrimination; racial minority; racial minority population; racism; recruit; resilience; segregation; social; stressor; theories

PROJECT SUMMARY Though the majority of the military is composed of non-Hispanic white men, the number of racial/ethnic minority and women veterans has steadily increased in recent years. African-American/Black and Hispanic veterans make up the largest proportion of the minority veteran population (about 50% and 30% of minority veterans, respectively) and the number of women veterans, especially racial/ethnic minority women, is expected to grow. Noted differences in alcohol use and behavioral health symptoms of PTSD and depression, as well as behavioral health care access, have been reported for post-9/11 African-American/Black, Hispanic veterans, and women veterans. However, research on disparities in veterans’ behavioral health care service utilization and behavioral health symptomology primarily focuses on veterans’ use of VA facilities, therefore missing 50% of veterans not receiving VA care. Unfortunately, there is limited research on the associations between perceived discrimination and quality of behavioral health care or behavioral health care access among racial/ethnic minority and women veterans who receive care outside of the VA system. Further, the studies that do exist on disparities among veterans tend to be cross-sectional and they limit their examination of disparities to individual factors only and do not incorporate larger environmental factors. Thus, the current study seeks to extend prior work by recruiting 2,000 non-VA attending veterans with 4 years of follow up (bi annual assessments). First, in an attempt to better understand veterans’ experiences of discrimination, 65 veterans will be recruited for in depth qualitative interviews. Results of these interviews will inform (not determine) measurement in the larger study. Once completed, recruitment will begin for the longitudinal cohort. We will oversample for racial/ethnic minority veterans (70% of the total recruited sample; n =1,400) and women veterans (40% of both racial/ethnic minority and non-Hispanic white recruited veterans; n = 800). Outcome data will be collected on behavioral health symptoms, including alcohol use/disorder, PTSD, and depression, as well as behavioral health care access (alcohol use treatment receipt, preparatory behaviors, attitudes about treatment). Experiences of racial discrimination and sexism (e.g., dignity denial, microaggressions, gender discrimination, sexual harassment, health care discrimination) as well as minority based and military specific stressors will be collected each wave. In addition to individual level data, we will use publicly available datasets (e.g., Census) to gather data on neighborhood deprivation, poverty, income inequality, segregation, neighborhood violence, distance to health care facilities, and liquor/alcohol outlets. We will use machine learning models that incorporate all hypothesized predictors across both individual and environmental domains to determine which factors are most important in predicting behavioral health symptoms, care receipt, and attitudes, and the valence of individual predictor effects on outcomes. The latter can provide a rank order of importance of predictors to help guide future prevention, intervention, and policy efforts.

项目摘要 虽然大多数军队是由非西班牙裔白色男子，种族/民族的人数 近年来，少数民族和妇女退伍军人人数稳步增加。非裔美国人/黑人和西班牙裔 退伍军人占少数民族退伍军人人口的最大比例（约占少数民族的50%和30%）。 退伍军人人数，特别是少数种族/民族妇女人数， 预计将增长。注意到饮酒和创伤后应激障碍和抑郁症的行为健康症状的差异， 据报道，9/11事件后，非洲裔美国人/黑人、西班牙裔 退伍军人和女性退伍军人。然而，对退伍军人行为卫生保健服务差异的研究 利用率和行为卫生学主要关注退伍军人对退伍军人管理局设施的使用，因此， 50%的退伍军人没有得到退伍军人管理局的照顾。不幸的是，对这些关联的研究有限， 感知歧视与行为医疗服务质量或行为医疗服务可及性之间的关系 在种族/少数民族和女性退伍军人谁接受照顾以外的退伍军人管理局系统。此夕h 确实存在的关于退伍军人之间差异的研究往往是横截面的，它们限制了他们的检查 仅考虑个人因素的差异，不考虑更大的环境因素。因此电流 一项研究试图通过招募2,000名非退伍军人管理局参加的退伍军人进行为期4年的随访（两年）来扩展先前的工作 年度评估）。首先，为了更好地了解退伍军人遭受歧视的经历， 将招募退伍军人进行深入的定性访谈。这些采访的结果将告知（不 在更大的研究中确定）测量。完成后，将开始招募纵向队列。 我们将对种族/少数民族退伍军人（占总招募样本的70%; n = 1，400）和女性进行过采样 退伍军人（40%的种族/少数民族和非西班牙裔白色招募的退伍军人; n = 800）。结果 将收集行为健康症状的数据，包括酒精使用/障碍，创伤后应激障碍和抑郁症， 以及行为卫生保健的获得（酒精使用治疗收据，准备行为， 治疗）。种族歧视和性别歧视的经历（例如，尊严否认，微攻击，性别 歧视、性骚扰、保健歧视）以及基于少数群体和针对军人的歧视 每一波都将收集压力源。除了个人层面的数据，我们还将使用公开的数据集。 (e.g.,人口普查）收集关于邻里剥夺、贫困、收入不平等、隔离、 邻里暴力、离医疗保健设施的距离以及酒类销售点。我们将使用机器 学习模型，包括所有假设的预测跨个人和环境域 确定哪些因素在预测行为健康症状、护理接受情况和 态度，和效价的个人预测结果的影响。后者可以提供以下等级顺序 预测因素对帮助指导未来的预防、干预和政策努力的重要性。