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.

项目总结 尽管军队中的大多数由非西班牙裔白人男性组成，但种族/民族的数量 近年来，少数民族和女性退伍军人人数稳步增加。非裔美国人/黑人和西班牙裔 退伍军人在少数民族退伍军人人口中所占比例最大(约占少数民族的50%和30% 退伍军人)和女退伍军人人数，特别是种族/少数民族妇女人数。 预计将会增长。注意到创伤后应激障碍和抑郁症在酒精使用和行为健康症状方面的差异， 以及行为卫生保健，据报道，9/11事件后，非裔美国人/黑人，西班牙裔 退伍军人和女退伍军人。然而，关于退伍军人行为保健服务差距的研究 利用和行为健康症状学主要关注退伍军人对退伍军人设施的使用，因此 失踪的退伍军人中有50%没有接受退伍军人管理局的护理。不幸的是，关于这种联系的研究有限。 感觉到的歧视与行为保健或行为保健获得的质量之间的关系 在接受退伍军人制度以外的护理的种族/少数民族和女性退伍军人中。此外， 确实存在的关于退伍军人之间差异的研究往往是横断面的，它们限制了他们的检查 这种差异仅限于个别因素，没有纳入更大的环境因素。因此，当前的 这项研究寻求通过招募2,000名非退伍军人身份参加的退伍军人来延长之前的工作，并进行4年的跟踪(双年 年度评估)。首先，为了更好地了解退伍军人遭受歧视的经历，65 退伍军人将被招募进行深入的定性面试。这些面谈的结果将告知(不 确定)在更大的研究中进行测量。一旦完成，将开始纵向队列的招募。 我们将对种族/少数民族退伍军人(占招募样本总数的70%；n=1400)和妇女进行过抽样。 退伍军人(40%的少数族裔和非西班牙裔白人都招募了退伍军人；n=800)。结果 将收集有关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， 以及行为保健服务(酒精使用治疗收据、准备行为、对 治疗)。种族歧视和性别歧视的经历(例如，尊严剥夺、微侵犯、性别 歧视、性骚扰、医疗保健歧视)以及基于少数群体的和特定于军队的歧视 每一波都会收集压力源。除了个别级别的数据外，我们还将使用公开可用的数据集 (例如，人口普查)，以收集关于社区剥夺、贫困、收入不平等、种族隔离、 社区暴力，距离医疗机构，以及酒类/酒类销售点。我们将使用机器 将所有假设的预测者纳入个人和环境领域的学习模型 确定哪些因素在预测行为健康症状、护理接受情况和 态度，以及个体预测因素对结果的影响的有效性。后者可以提供以下排序 预测因素在帮助指导未来的预防、干预和政策努力方面的重要性。