The SMART-CV Study: Systems Modeling Approaches to Reducing Disparities in Cardiovascular Diseases

摘要在美国，心血管疾病(CVD)是导致死亡的主要原因， 这个国家。尽管正在进行预防工作，但社会和经济上处于不利地位的人口仍在继续 承担心脑血管疾病的最高负担。越来越多的证据表明，健康的社会决定因素可以起到 在心血管疾病中造成这种差异的作用。然而，目前旨在减少心血管疾病差异的干预措施 很少关注多个领域和水平的心血管健康的社会决定因素 有影响力，但倾向于关注个人层面的因素。此外，在设计干预措施时，政策制定者 必须考虑最大限度提高普通人群健康的干预措施与那些 将健康差距降到最低，如果不能在两者之间取得平衡，就会导致健康状况的提高 差距和次优人口健康。因此，为了有效地减少心血管疾病-冠状动脉病变的差异 心脏病和中风，迫切需要在设计 可持续和负担得起的有针对性的健康干预措施，整合各领域和各层次的社会因素 它针对的是社会和经济上处于不利地位的人群。这份K01提案将 应用几种分析方法，包括关键线人访谈、综合控制法和 评估干预影响和成本的模拟建模和系统科学(SMSS)方法 有可能有效减少心血管疾病健康差距的战略。为了执行这一提议，Dr。 Nianogo将(1)确定加州实施的干预措施和政策，重点是 跨多个领域和影响程度的心血管健康的社会决定因素，(2)调查 三种社会政策干预对心血管疾病发病率的影响；(3)开发计算机模拟模型，以 预测针对弱势群体的干预措施和政策的长期影响和成本效益 亚群，高危亚群。这项研究的发现有助于确定有针对性的健康干预措施 这有可能减少心血管疾病的健康差距，并为政策决策提供信息。 K01提案建立在候选人之前的研究基础上，该研究开发了计算机模拟 研究肥胖和2型肥胖症患病率和发病率的模型(虚拟洛杉矶队列-Vila) 在洛杉矶出生并从出生到年龄被跟踪的具有代表性的美国人中的糖尿病 65.这项提议将在具有足够广泛的强有力的指导的背景下进行 在干预研究、心血管疾病、健康差异和模拟建模方面的经验和专业知识 对于这个项目的成功来说是必不可少的。加州大学洛杉矶分校为进行这种创新提供了足够的环境 研究。在整个提案中，候选人将获得SMSS和CVD差异研究方面的技能；这些 技能是为Nianogo博士提供成为成功的独立医生的手段和知识的关键 在计算流行病学和SMSS方面的研究人员，并确保未来的R01资金。

ABSTRACT Cardiovascular diseases (CVDs) represent the leading cause of death in the US and are costly to the nation. Despite ongoing prevention efforts, socially and economically disadvantaged populations continue to bear the highest burden of CVDs. There is growing evidence that social determinants of health can play a role in producing such disparities in CVD. Yet current interventions designed to reduce disparities in CVD seldom focus on the social determinants of cardiovascular health across multiple domains and levels of influence but tend to focus on individual-level factors. Furthermore, when designing interventions, policymakers have to consider the trade-offs between interventions that maximize health in the general population and those that minimize health disparities and failure to reach a balance between these can result in increased health disparities and suboptimal population health. Therefore, to efficiently reduce disparities in CVD—coronary heart diseases and stroke, there is a pressing need for assisting policy decision-making in the design of sustainable and affordable targeted health interventions that integrate social factors across domains and levels of influence and which targets the socially and economically disadvantaged populations. This K01 proposal will apply several analytic approaches including key informant interviews, the synthetic control method and simulation modeling and systems science (SMSS) methods to evaluate the impact and costs of intervention strategies that have the potential to efficiently reduce CVD health disparities. To carry out this proposal, Dr. Nianogo will (1) identify interventions and policies that are implemented in California which focused on the social determinants of cardiovascular health across multiple domains and levels of influence, (2) investigate the impact of three societal policy interventions on CVD incidence; (3) develop a computer simulation model to project the long-term impact and cost-effectiveness of interventions and policies targeting disadvantaged subpopulations, high-risk subpopulations. Findings from this study help identify targeted health interventions that have the potential to reduce health disparities in CVD and inform policy decision-making. This K01 proposal builds on the candidate’s previous research that developed a computer simulation model (Virtual Los Angeles Cohort—ViLA) to study the prevalence and incidence of obesity and type 2 diabetes among a representative sample of U.S. persons born in Los Angeles and followed from birth to age 65. The proposal will be pursued within the context of a strong mentorship that has adequate extensive experience and expertise on intervention research, CVDs, health disparities and simulation modeling that is necessary for the success of this project. UCLA provides an adequate environment to conduct such innovative research. Throughout this proposal, the candidate will gain skills in SMSS and CVD disparities research; these skills are critical to providing Dr. Nianogo with the means and knowledge to become a successful independent investigator in computational epidemiology and SMSS and to secure future R01 funding.