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