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

心脏代谢和精神健康状况非常普遍，并且彼此的管理复杂化 当它们同时发生时，会导致更大的残疾和医疗费用。这样的多元共融， 当它与种族不平等和健康的社会决定因素相交时， 不良后果的风险增大。成功的疾病管理适用于以下几个方面的交叉点 个人和社区必须实施，以减少差距，最佳待遇必须解决 在与文化相关的干预措施中，心脏代谢和精神健康疾病的各个方面。循证 没有证据表明干预措施在文化上与资源贫乏的少数民族人口相关。的 社区观点可能在共病状况的整体护理管理中缺失。科学研究 必须将少数群体纳入数据收集和社区确定工作， 成功管理合并症的重要变量。随后，定位这些变量 在社区产生的概念模型，然后可测试的计算机模型将有助于确定和强调 制定与文化相关的干预战略的要点。K01候选人提出了一个平行的， 了解社区观点、分析风险因素和决定因素的综合动态过程， 建立和测试解释差异和预测结果的计算机模型。Connecting the dots 流行病学研究和通过系统科学建立的计算机模型之间的联系， 参与性框架将允许制定准确的、可复制的健康差距模型， 由社区的声音和人口科学家共同建立。 .

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. .