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. .

心脏代谢性疾病和精神健康疾病非常普遍，彼此之间的管理也很复杂 当它们同时发生时，会导致更大的残疾和医疗成本。这样的多种共病变得均匀 当它与种族不平等和健康的社会决定因素相交时更具挑战性，最终导致 放大了不良后果的风险。成功的疾病管理在以下方面发挥作用 必须实施个人和社区，以缩小差距，必须解决最佳治疗问题 在与文化相关的干预中心脏代谢障碍和精神健康障碍的方面。以证据为基础 尚未证明干预措施对资源贫乏的少数群体具有文化相关性。这个 在合并症的整体护理管理中，社区视角可能会缺失。科学研究 必须将少数群体既包括在数据收集中，也包括在确定的社区中 对成功处理并存情况很重要的变量。随后，定位这些变量 在社区生成的概念模型中，然后是可测试的计算机模型将有助于识别和突出 制定与文化相关的干预策略的要点。K01候选人提出了一个类似的方案， 理解社区观点、分析风险因素和决定因素的综合动态过程， 以及建立和测试解释差异和预测结果的计算机模型。把这些点连在一起 以社区为基础的流行病学研究和通过系统科学进行计算机建模之间的关系 参与性框架将允许开发准确的、可复制的健康差距模型，这些模型 由社区声音和人口科学家共同建造。 。