Utilizing mixed methods to understand social determinants of successful disease management among populations with comorbid cardiometabolic syndrome and anxiety disorder

利用混合方法了解患有心脏代谢综合征和焦虑症的人群中成功进行疾病管理的社会决定因素

• 批准号: 10808379
• 负责人: Tyra Dark
• 金额: $ 13.02万
• 依托单位: FLORIDA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-24 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10808379
• 关键词: Address; Affect; Anxiety; Anxiety Disorders; Back; Big Data; Characteristics; Clinical Management; Communities; Complex; Computer Models; Darkness; Data; Data Analyses; Data Collection; Development; Disease; Disease Management; Disparity; Economics; Epidemiology; Equity; Ethnic Origin; Evidence based intervention; Eye; Feedback; Foundations; Future; Goals; Health; Health Care Costs; Health Personnel; Healthcare; Individual; Intervention; Managed Care; Maps; Measures; Medical; Mental Health; Mental disorders; Mentored Research Scientist Development Award; Methodology; Methods; Minority; Minority Groups; Modeling; Not Hispanic or Latino; Outcome; Pattern; Persons; Population; Population Group; Positioning Attribute; Prevalence; Process; Process Assessment; Psyche structure; Race; Research; Research Personnel; Resources; Risk; Risk Factors; Science; Scientist; Specific qualifier value; Syndrome; System; Techniques; Testing; Training; Translating; Trust; Visualization; Voice; Work; adverse outcome; analytic epidemiology; anxiety-related disorders; black men; black women; burden of illness; cardiometabolism; community based participatory research; community building; comorbidity; cultural competence; design; disability; disparity reduction; epidemiologic data; epidemiology study; ethnic minority; evidence base; health disparity; health equity; integrated care; member; minority communities; optimal treatments; outcome prediction; racial disparity; resilience factor; skill acquisition; skills; social determinants; social health determinants; social influence; underserved minority

Cardiometabolic and mental health conditions are highly prevalent and complicate management of each other when they co-occur, leading to greater disability and healthcare costs. Such multi-comorbidity becomes even more challenging when it intersects with racial inequities and social determinants of health culminating in amplified risk of adverse outcomes. Successful disease management that works at the intersection of the individual and the community must be implemented to mitigate disparities and optimal treatment must address aspects of both cardiometabolic and mental health disorders in a culturally relevant intervention. Evidence based interventions have not been demonstrated to be culturally relevant for resource poor minority populations. The community perspective is likely missing in the overall care management of comorbid conditions. Scientific study must include minorities both in the collection of data and also in the identification of community identified variables important in successful management of comorbid conditions. Subsequently, positioning these variables in community-generated conceptual models and then testable computer models will help to identify and highlight key points for development of culturally relevant intervention strategies. The K01 candidate proposes a parallel, integrated dynamic process of understanding community perspectives, analyzing risk factors and determinants, and building and testing computer models that explain disparities and predict outcomes. Connecting the dots between epidemiologic research and computer modeling through systems science in a community based participatory framework will allow for the development of accurate replicable models of health disparities that are co-built by community voices and population scientists. .

心脏代谢和精神健康状况非常普遍，并使彼此的管理复杂化