Using Systems Science Methods to Study Cardiac Risk in the Somali Community

描述：代谢综合征是一种需要临床干预的心血管疾病（CVD）的多重危险因素。社会因素对代谢综合征（CMS）组成部分的影响已有大量文献记载。社会因素如何影响CMS以及不同的干预措施如何通过影响社会因素来降低CMS的患病率的证据非常薄弱。在2009年美国近1250万法律的永久居民的情况下，证据就更弱了。目前的还原论分析方法来理解社会环境对CMS的影响是不足以理解的非线性，关系驱动系统的复杂性。明尼阿波利斯的索马里社区提供了一个独特的机会，利用基于联系追踪的抽样技术收集数据，答复率非常高。由于社区的紧密联系和密集分布，项目的社区驱动性质，以及研究团队以前的成功，良好的声誉和社区的善意，预计会有很高的参与度。该项目的结果可能会对该社区产生直接影响。公共卫生的一般将大大受益，因为这项研究将提供一个系统的方法为基础的分析框架，以评估潜在的和结构性的机制连接社交网络CMS。它还将提供基于模拟健康干预措施的前所未有的见解。这一框架将为决策者提供一个未来在社会环境和CMS之间进行干预的地图。该项目分为三个阶段。第一阶段：第二阶段：社会网络分析（SNA）和假设检验;第三阶段：基于SNA结果的参数化ABM，验证ABM，并检验更多的假设。我们将从四个主要数据来源收集数据：包括人体测量在内的面对面访谈，生物样本，HealthPartners健康计划的保险索赔，以及州和联邦保险个人从人类服务部的索赔。ABM将使我们能够比较各种干预措施的效果，如引入社区卫生工作者（CHW），以增加社区中心、医院和诊所外展位置的卫生保健供应，对非索马里医生进行更好的文化培训，通过不同渠道（例如，大众媒体、社区领导人、论坛、中心和协会、口译员），以及更好地认识和获得预防服务。在其他旨在改善社区健康结果和行为的研究中，将这些干预措施纳入一个创新、有效和全面的分析框架是一个重要的遗漏。

DESCRIPTION: Metabolic syndrome is a multiplex risk factor for cardiovascular disease (CVD) in need of clinical interventions. The impact of social factors on the components of metabolic syndrome (CMS) is well documented. The evidence for how social factors impact CMS and how different interventions can reduce the prevalence of CMS through influence on social factors is very weak. The evidence is weaker in the case of nearly 12.5 million legal permanent residents in the United States in 2009. The current reductionist analytical approaches to understanding the effect of social environment on CMS are inadequate to understanding the complexities of nonlinear, relationship-driven systems. The Somali community in Minneapolis presents a unique opportunity for collecting data using link tracing- based sampling techniques, with a very high response rate. Due to the tightly knit, densely located nature of the community, the community-driven nature of the project, and the research team's previous successes, excellent reputation, and goodwill in the community, very high participation is anticipated. Findings from the project will likely have a direct impact on this community. Public health in general will benefit significantly because this study will provide a systems approach-based analytical framework to evaluate the underlying and structural mechanisms linking social networks to CMS. It will also provide unprecedented insights based on simulated health interventions. This framework will give policymakers a map for future interventions between the social environment and CMS. The project has three stages. Stage 1: Respondent-driven sampling (RDS)-based data collection and construction and design of an agent-based model (ABM) framework; Stage 2: social network analysis (SNA) and testing of hypotheses; Stage 3: parameterize ABM based on results from SNA, validate the ABM, and test more hypotheses. We will collect data from four primary data sources: in-person interviews including anthropometric measures, biological samples, insurance claims from the HealthPartners health plan, and state and federally insured individuals' claims from the Department of Human Services. The ABM will allow us to compare the effects of such varied interventions as the introduction of community health workers (CHWs) for increasing the supply of heath care in an outreach position in community centers and in hospitals and clinics, better cultural training of non-Somali physicians, increased communication through different channels (e.g., mass media, community leaders, forums, centers and associations, interpreters), and better awareness of and access to preventive services. Bringing together such interventions in an innovative, validated, and comprehensive analytical framework has been an important omission in other research aimed at improving the health outcomes and behaviors of communities.