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

描述：代谢综合征是心血管疾病(CVD)的多重危险因素，需要临床干预。社会因素对代谢综合征(CMS)的影响是有据可查的。关于社会因素如何影响CMS以及不同干预措施如何通过影响社会因素来降低CMS患病率的证据非常薄弱。在2009年美国近1250万合法永久居民的案例中，证据就更弱了。目前用于理解社会环境对CMS影响的简约主义分析方法不足以理解非线性的、关系驱动的系统的复杂性。明尼阿波利斯的索马里社区提供了一个独特的机会，可以使用基于链接追踪的抽样技术收集数据，回复率非常高。由于社区的紧密联系和密集分布的性质，该项目的社区驱动型性质，以及研究团队以前在社区中的成功、良好的声誉和善意，预计会有非常高的参与度。该项目的发现可能会对这个社区产生直接影响。总体来说，公共卫生将大大受益，因为这项研究将提供一个基于系统方法的分析框架，以评估将社会网络与合作医疗联系起来的潜在和结构性机制。它还将在模拟健康干预的基础上提供前所未有的见解。这一框架将为政策制定者提供未来社会环境和CMS之间干预的地图。该项目分为三个阶段。阶段1：基于受访者驱动抽样(RDS)的数据收集和基于代理的模型(ABM)框架的构建和设计；阶段2：社会网络分析(SNA)和假设检验；阶段3：基于SNA的结果对ABM进行参数化，验证ABM，并检验更多假设。我们将从四个主要数据来源收集数据：面对面采访，包括人体测量、生物样本、HealthPartners医疗计划的保险索赔，以及州和联邦保险个人从公共服务部的索赔。ABM将使我们能够比较各种干预措施的效果，例如在社区中心、医院和诊所的外展岗位引入社区卫生工作者(CHW)以增加卫生保健的供应，对非索马里医生进行更好的文化培训，通过不同渠道(如大众媒体、社区领袖、论坛、中心和协会、翻译)加强沟通，以及提高对预防服务的认识和获得预防服务的机会。在旨在改善社区健康结果和行为的其他研究中，将这些干预措施汇集在一个创新的、有效的和全面的分析框架中一直是一个重要的遗漏。

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.